As customer recommendation by word-of-mouth can attract more customers, this research deals with Customer recommendation prediction for a furniture store like IKEA. This research compares the machine learning models for Recommendation, namely, Artificial Neural Network (ANN), Logistic Regression (LR), and Linear Discriminant Analysis (LDA). The research is based on customers' feedback about the first store in Hyderabad, India. In this research work, IKEA's service is explored, and the models created are successful based on the predictor variables used in the study as the model's inputs. The abovementioned techniques were used to predict the customer recommendation of IKEA to their neighbours, family, and friends. It is evident from the classification outcomes of different models that ANN outperforms the other techniques, namely, the Logistic Regression model and LDA. As a managerial implication, this model could be used by any furniture or multinational retail stores to predict their customer recommendation status to improve their market and profit. Customer recommendation prediction in national/multinational stores, especially retail furniture stores, does not exist in the literature. This research aims to fill the gap in the literature to predict the customer recommendation status of retail furniture stores or any other multinational stores
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