A Novel Utility and Frequency Based Itemset Mining Approach for Improving CRM in Retail Business

The paradigm shift from ‘data-centered pattern mining’ to ‘domain driven actionable knowledge discovery’ has increased the need for considering the business yield (utility) and demand or rate of recurrence of the items (frequency) while mining a retail business transaction database. Such a data mining process will help in mining different types of itemsets of varying business utility and demand. We here present a set of algorithms for mining all types of utility and frequency based itemsets from a retail business transaction database which would significantly aid in inventory control and sales promotion. This set of algorithms are also capable of identifying the active customers of each such type of itemset mined and rank them based on their total or lifetime business value which would be extremely helpful in improving Customer Relationship Management (CRM) processes like campaign management and customer segmentation.