What is Data Mining?
Data mining is the process of finding useful patterns and relationships in large volumes of data. This uses statistical algorithms and models to find trends from existing data warehouses.
Role of Data Mining in CRM
Although it’s still a new technology, businesses from many industries have invested in it to make the most of their data. Data mining techniques in CRM assist your business in finding and selecting relevant information. This can then be used to get a clear view of the customer life-cycle. The life-cycle includes customer identification, attraction, retention, and development. The more data in the database, the more accurate the models created will be and hence more value gained.
Data mining usually involves the use of predictive modeling, forecasting, and descriptive modeling techniques as its key elements. CRM in the age of data analytics enables an organization to engage in many useful activities. You can manage customer retention, choose the right segments, set optimal pricing policies, and rank suppliers to your needs.
Applications of Data Mining in CRM
Basket Analysis
Find out which items customers tend to purchase together. This knowledge can improve stocking, store layout strategies, and promotions.
Sales Forecasting
Examining time-based patterns helps businesses make re-stocking decisions. Furthermore, it helps you in supply chain management, financial management and gives complete control over internal operations.
Database Marketing
Retailers can design profiles of customers based on demographics, tastes, preferences, and buying behavior. It will also aid the marketing team in designing the right marketing campaigns and promotional offers. This will result in enhanced productivity, optimal allocation of resources, and desirable ROI.
Predictive Life-Cycle Management
Data mining helps an organization predict each customer’s lifetime value and service each segment properly.
Market Segmentation
Learn which customers are interested in purchasing your products. Design your marketing campaigns and promotions keeping their tastes and preferences in mind. This will increase efficiency and result in the desired ROI since you won’t be targeting customers who are not interested in your product.
Product Customization
Manufacturers can customize products according to the exact needs of customers. To do this, they must be able to predict which features should be bundled to meet customer demand.
Fraud Detection
By analyzing past transactions that turned out to be fraudulent, you can take precautions to stop that from happening again. Banks and other financial institutions will benefit from this feature immensely, by reducing the number of bad debts.
Warranties
Manufacturers need to predict the number of customers who will make warranty claims and the average cost of those claims. This will ensure the best management of company funds.
Learn more about Big Data and its relevance to CRM.
Techniques for Data Mining in CRM
Anomaly Detection
When you search for information that doesn’t match expected behavior or a projected pattern, that is anomaly detection. Anomalies can provide actionable information because they deviate from the average in the data set.
Association Rule Learning
Discover relations between data items in huge databases. With Association Rule Learning you can uncover hidden patterns, use that to better understand customers.
Clustering
Identify similar data sets and understand both the similarities and the differences within 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.
Classification
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
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 as a result of improved ability to respond to each contact and reduced costs due to the optimal allocation of resources.
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