Applications of Data Mining in CRM

4 min readOct 4, 2017


What is Data Mining?

Data mining is the process of unearthing useful patterns and relationships in large volumes of data. A sophisticated data search capability that uses statistical algorithms to uncover patterns and correlations, data mining extracts knowledge buried in corporate data warehouses.

Role of Data Mining in CRM

Although it is still a relatively new technology, businesses from all industry verticals i.e. healthcare, manufacturing, financial, transportation etc. have invested in it to take advantage of historical data. Data mining techniques in CRM assist your business in finding and selecting the relevant information that can then be used to get a holistic view of the customer life-cycle; this comprises of four stages: customer identification, customer attraction, customer retention, and customer development. The more data there is in the database, the better the models will be created and their subsequent use will result in more business value.

Data mining typically involves the use of predictive modeling, forecasting and descriptive modelling techniques as its key elements. Exploiting CRM in this age of data analytics enables an organization to manage customer retention, select the right prospects & customer segments, set optimal pricing policies, and objectively measure and rank which suppliers are best suited for their needs.

Applications of Data Mining in CRM

1. Basket Analysis

Ascertain which items customers tend to purchase together. This knowledge can improve stocking, store layout strategies, and promotions.

2. Sales forecasting

Examining time-based patterns helps businesses make stocking decisions. Furthermore, it helps you in supply chain management, planning your financials and gives complete control over internal operations.

3. Database marketing

Retailers can design profiles of customers based on their demographics, tastes, preferences, buying behavior etc. It will also aid the marketing team in designing personalized marketing campaigns and promotion offers.

This will result in enhanced productivity, optimal allocation of the company’s resources and bring the desired ROI.

4. Predictive life-cycle management

Data mining helps an organization predict each customer’s lifetime value and to service each segment appropriately.

5. Customer segmentation

Learn which customers are interested in purchasing your products and design your marketing campaigns and promotions keeping their tastes and preferences in mind. This will increase the efficiency and result in the desired ROI since you won’t be targeting customers who show little to no interest in your product.

6. Product Customization

Manufacturers can customize products according to the exact needs of customers. In order to do this, they must be able to predict which features should be bundled to meet the customer demand.

7. Fraud detection

By analyzing past transactions that were later determined to be fraudulent, a business can take corrective measures and stop such events from occurring in the future. Banks and other financial institutions will benefit from this feature immensely.

8. Warranties

Manufacturers need to predict the number of customers who will submit warranty claims and the average cost of those claims. This will ensure efficient and effective management of company funds.

Learn more about the future of CRM in our post on the Internet of Things.

Techniques for Data Mining in CRM

Anomaly Detection

Searching for information that doesn’t match expected behavior or a projected pattern is called anomaly detection. Anomalies can provide actionable information because they deviate from the average in the dataset.

Association Rule Learning

Discover relations between data items in huge databases. With association rule learning, hidden patterns can be uncovered and the information gained may be used to better understand customers, learn their habits, and predict their decisions.


Identify similar data sets and understand both the similarities and differences between the data. Data sets that have similar traits can be used for conversion rate increases. For example, if the buying behavior of one group of customers is similar to that of another group, they can both be targeted with similar services or products.


This technique is used for gathering information about data so that the data sets can be placed into proper categories. One example is the classification of email as either regular, acceptable email or as spam.


Regression analysis is one of the advanced data mining techniques in CRM. The objective is to find the dependency between different data items and map out which variables are affected by other variables. This technique is used to determine customer satisfaction levels and their impact on customer loyalty.

Bottom Line:

Data mining together with the rise of Artificial intelligence will shape the future of CRM and aid companies in their quest to become more customer-oriented. The combination of CRM and DM tools will augment the knowledge and understanding of customers, products and transactional data, thereby improving strategic decision making and tactical marketing activity. The outcome? Increased revenue due to improved ability to respond to each individual contact and reduced costs due to the optimal allocation of resources.

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