International tourist hotel industry has becoming a professional management domain nowadays. Due to the increasingly fierce competition, hotels must develop ways to attract customers by meeting their market requirements and preferences, rather than waiting passively for the customers to come. With data mining technology, the hotels can facilitate the discovered characteristics of potential customers to make the right marketing strategies and decisions by targeting at specific groups of customers. Behind the consumers¡¦ behaviors, there are usually indicators for special consuming requirements. However, by browsing the business transaction data, one can usually learn only the consuming requirement volume and is unable to determine the implied and hidden information. This research makes use of data mining technology to explore the customers¡¦ historical data. Specifically, it applies the discovered decision rule to investigate and validate the characteristics of potentially customers ¡X customers who are more likely to book rooms of higher rate. We apply the data mining techniques to the transactional data of a hotel, collected over three years. Our research reveals that there exist characteristics rules for the potential customers and these rules do not change abruptly over the years. The application of these rules to target advertising in hotel domain is verified using the hotel transaction data collected in the subsequent year. The result shows that by targeting at customers of the discovered characteristics rules, higher response rate can be achieved.