In recent years, an element of CRM; eCRM has become a topic of major importance. However, the wireless medium as an element of CRM is rarely taken into consideration and the literature concerning mCRM is scarce. In an attempt to fill this void, this dissertation provides insights into mCRM and data mining solution for mining customer’s information from customer opt-in database. We divide the dissertation into two segments. The first segment investigates a new data mining technique and compares it with the classification based on associations (CBA) for mining classification rules from different data sets. Our new approach, LCA is introduced and rules are generated with both CBA and LCA approach by using the Apriori algorithm. The LCA is very effective in terms of reducing the number of combinations of the item sets in each iteration; hence will be capable of reducing the system response time for generating rules from mobile customer opt-in database. The second segment is oriented around mCRM. Our study endeavor to build an empirically grounded framework of the initiation stage of (mCRM) in retailing. The main result of this study indicates that mCRM may be an effective element to CRM strategy, if customer relation is based on permission marketing and trust. By collecting and maintaining useful information through data mining from the customers’ database, stores can offer their customers interesting services via the mobile medium (SMS/MMS) and can retain customers with different ways and maintain fruitful relations with their customers based on trust.