Abstract
•Adapting methods to explore attributes’ influence and detect objects’ siblings.•Apply information retrieval, knowledge discovery, and opinion mining techniques.•Provide a competitive map for retaining customer loyalty.•Succeed to enlarge the customers’ segment.•Apply the experiment successfully on two real datasets.
Nowadays, firms are strongly racing to raise their competitive level in the international market. As this market has a natural connection, therefore, one of the vital roads for competing is exploring the users’ behaviour which continuously changes over time. This research proposes an intelligent information retrieval-based framework which applies a set of techniques to monitor the customers’ behaviour and determine the behaviour similarity. These techniques followed a determined opinion mining, knowledge discovery, weight measurement, and text analysis approaches. The aim of the proposed framework is to explore the suitable recommendations for the current customers and acquire new customers who could be selected from the customers’ social friends, which leads to the increase of the market share, a raise in the loyal customers’ segment, and finally in the firm’s competitiveness level. The framework has been successfully verified in two successful companies, the evaluation included different measures such as responding to change rate, and the customers’ segment share percentage. The evaluation presented an increase in the customers’ satisfaction level to be 97.91% and 97.31% for the two companies respectively while the willingness of new customers to join the customers’ segment has been raised by 83.15%. while However, the study could be further expanded in many directions such as the discovery of the customers’ opinion based on different sentiment levels.