Customer recommendation model to support real estate salespersons for a restaurant

In this paper, we propose a recommendation model of application customer to reduce the time and cost of the salesperson by recommending the application customer to salesperson. The model is built based on customer information, premise information, and two new features which are extracted from salesperson’s feedback. Estimation precision is evaluated by three algorithms: SVM, Decision Tree and Random Forest. We applied three algorithm against five data sets. As a result, the highest estimation precision was 49.2% using RF against the data which selected by some important features (data 5). In the case of using the customer information and property information (data 3), estimation precision was 38.7%. Moreover in the case of adding new two features against that data (data 4), estimate precision was 44.2%. From comparison between data 3 and data 4, we clarified that the new two features increased the estimation precision. Also, from comparison between data 4 and data 5, we clarified that selecting some important features increased the estimation precision. In addition, according to the feedback from the ABC TENPO Inc which we are conducting joint research, our model increased the precision compared with the veteran salesperson.