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Purpose Online customer relationship management (CRM) is an important issue for implementing digital marketing of electronic commerce or social commerce. The purpose of this study is to establish valuable markets for discovering customer knowledge from data-driven CRM systems for enhancing growth rates of businesses. Airline or travel agency industries are online businesses in the world. Therefore, the industries in Taiwan will be an empirical case for this study. Design/methodology/approach This research applied a procedure with an applied proposed model for establishing valuable markets from data-driven CRM systems. However, the study used a proposed customer value model (recency, frequency and monetary [RFM]; RFM model-based), the analytic hierarchy process (AHP) procedure and a proposed equation for estimating customer values. Findings For enhancing the data-driven CRM marketing of the industries, in this research, the market of air travelers can be partitioned into eight markets by the proposed model. As well, the markets can be ranked by the AHP procedure. Furthermore, the travelers’ customer values can be estimated by a proposed customer value equation. Originality/value Via the applied proposed procedure, online airlines, travel agencies or other online businesses can implement the research procedure as their data-driven marketing strategy on their online large-scale or Big Data customers’ databases for enhancing sales rates.

This paper proposes a new model to discover customer value of air passengers by using data mining technologies.

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The purpose of this research is to mine high-value family travelers for CRM systems of online airlines and travel agencies. This research uses the data mining technologies to analyze the online travel market, which consists of clustering, decision tree, and analytic hierarchy process (AHP) procedure with a proposed model. In the research, the market of online air travel (ticket or package) shoppers is divided into six markets. The markets can be ranked via AHP procedure. The study also applies the C5?0 decision tree algorithm on the discovered ranked markets, transactional variables, and socioeconomic variables to create four useful classification rules. The discovered rules can be employed in web-based customer relationship management (CRM) marketing systems for airlines and online air travel agencies for enhancing the travelers' growth rates and customer values.