02 - Data Mining - Real World Scenario


Data mining has been very popular and widely accepted for the last few years. Data mining techniques have been applied in a number of industries including insurance, healthcare, finance, manufacturing, retail and so on. Of course, the process of applying data mining to complex real-world tasks is really challenging. However, industries try to make the best use of data mining which helps them make wise critical business decisions and hence gain competitive advantage.

Data Mining in Retail Industry

The strength of data mining is effectively used mainly by customer focused businesses. Such businesses always try to come up with new techniques and strategies that improve customer satisfaction. Retail industry has been applying data mining techniques to identify customer behavior and purchase patterns and trends and improve supply chain management. If applied correctly, data mining techniques can help retailers to improve customer retention and customer satisfaction and hence improve profit.

Let’s see how data mining can be applied in different areas of a retail business.

a) Product Pricing

Retailers can satisfy the customers in a number of ways. It can be by introducing price discounts or purchase coupons, coming up with a new store in a prime location, offering quality goods at reasonable rates and so on. The easiest method is to price the product correctly so that the new customers turn into regular customers. Product pricing needs to be done very carefully so that it is not only beneficial for the retailer, but also for the customers.

Optimizing the prices for all products in a store is, of course, a very difficult task. A number of factors such as consumer demand, price demand interactions, etc need to be considered while modifying the price of any product. Normally, price increases leads to lower sales and customer adoption of alternate products. Data mining can be used to identify consumer demand for the products and also to understand how the price change of a particular product affects sales of other products.

b) Purchase Trends

By analyzing the purchase history, retailers can understand the purchase behavior of customers which is helpful in many different ways. For example, if a large group of people purchase two or three items together, then the retailer can keep those items in adjacent locations. This will make sure that customer who wants to purchase those items would not miss them which in turn increases the sales. Another case is that if a retailer finds that more customers prefer to shop on weekends, then retailers can stock enough products (inventory management) and introduce attractive discounts on those days to attract more customers.

c) Customer Segmentation

If a retailer can segment the customers based on different factors, it will be useful for coming up with effective marketing strategies. Customers who respond to launch of specific products can be grouped together; customers who purchase same type of products can be grouped together; customers who shop on weekends can be grouped together; customers who respond to new promotions or discounts can be grouped together. This information can be useful to identity how different groups might react in general to specific offers, advertisements or promotions. Hence, retailers can take care of those customers when they introduce things that might attract that particular group.

d) Effectiveness of Promotions/Advertisements

It is a fact that not every advertisement or promotion is equally effective and successful. Data mining techniques can be used to identify how effective a particular promotion could be across different media or geographic locations. For example, data mining can be applied to check which segment of customers respond positively to a promotion, how effective the promotion could be in terms of cost and benefits, which media channels have been successful for different campaigns in the past and so on. By analyzing this kind of information, retailer can come up with more effective and fruitful promotions and advertisements.


Data mining techniques can not only be applied in the above specified areas of retail industry. Instead, it can also be applied on areas like customer lifetime value analysis, customer loyalty analysis, cross selling, target marketing, supply chain management, demand forecasting, inventory control and so on. All these data mining techniques (if applied correctly) enable retailers to improve the business and to stand out from the crowd.

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