“…For the second of these options, the information that can be drawn from loyalty cards may be particularly useful. Pauler and Dick (2005), for example, present a model in which customer loyalty-card data are used to segment households, with a view to maximizing the profitability of pricing and promotion policies based on elasticity estimates across segments. Tao and Yeh (2003) also present a method that can be applied to improve customer retention in the credit card industry.…”
Section: Loyalty Cards and The Information They Provide For Retail Mamentioning
“…For the second of these options, the information that can be drawn from loyalty cards may be particularly useful. Pauler and Dick (2005), for example, present a model in which customer loyalty-card data are used to segment households, with a view to maximizing the profitability of pricing and promotion policies based on elasticity estimates across segments. Tao and Yeh (2003) also present a method that can be applied to improve customer retention in the credit card industry.…”
Section: Loyalty Cards and The Information They Provide For Retail Mamentioning
“…Uncles 1994;Sharp and Sharp 1997;Lal and Bell 2003;Mauri 2003) show that loyalty schemes are of value to the supply side as they establish long term customer relationships, increase revenues, and generate valuable information about customers' shopping behavior (e.g. Ziliani and Bellini 2004;Pauler and Dick 2006). Loyalty, in terms of the share of visits to a specific store, and profits are found to be positively correlated (e.g.…”
“…In RFM model, Frequency and Monetary are whole-time aggregates. In fact, they do not reflect the value of newly joined customers who have the potential to become big spenders [14] [2]. Consequently, RFM will undervalue these customers.…”
Section: Rfmg Modelmentioning
confidence: 99%
“…One is to build customer loyalty. The other is to provide a source of useful data on customer behavior that can later be used to aid future CRM efforts [2].…”
Despite the proliferation of database marketing in the marketing engineering literature, there is little research that focuses on how to segment customers and find out the valuable customers within a reward programs database. The paper develops a RFMG model, which based on RFM model by creating gap coefficient to consider purchases made at competitors, to target valuable customers. The validity of our model is statistically tested using a reward program database of a shopping mall in Beijing. The results show that RFMG model not only can target more valuable customers than RFM model but also can find out the most valuable customer group in a reward program database.
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