The effects of recommender system on sales promotion of high-value products: Evidence from a field experiment in the real estate industry - LIU, Lian (Institutional Knowledge at Singapore Management University, 2022)
Real estate sales industry in China has long suffered the problem of inefficient matching of customers to projects. Inspired by the design of recommender systems, which have been widely used in the online retail industry, and are shown to facility customer-product matching and improve sales, we apply this system to the real estate sales industry using a novel approach. Instead of recommending products to customers, we suggest the best potential customers to salespeople with whom they will conduct sales with. Using city-wide sales data from the largest real estate sales company in China, we first develop a recommend system based on the predictive model where successful sales are explained by the combined characteristics of customers and real estate projects. We then conduct a field experiment to test the effectiveness of the system. We find that employing such system improves salespeople’s engagement with customers as well as customers’ willingness to visit, key contributors to successful sales.