This paper examines how the profitability of behavioral data in mobile marketing depends on firms' time preferences. It shows that the profitability of behavioral targeting can be neutral, positive, or negative, depending on factors such as consumers' transport costs and firms' discount factors. The study finds that when consumers have similar transport costs, it is costly to generate informative data on past purchases, which may lead to a loss of market share. However, if some consumers have very low transport costs, firms may compete fiercely for those consumers, even at close locations, which can reduce profits compared to a situation without behavioral data. The paper also highlights that the precision of behavioral targeting depends on the distribution of transport costs and the discount factor. When transport costs are more heterogeneous, the effect of behavioral pricing on profit is more likely to be negative. The study contributes to the literature on behavior-based price discrimination and mobile geo-targeting by showing that the distribution of transport cost and the discount factor are crucial for predicting the profit and welfare effects of combining behavior-based pricing with mobile geo-targeting. The paper also demonstrates that the profitability of behavioral data may depend on firms' time preferences, and that firms may choose first-period prices to obtain more precise information. The results show that the profitability of behavioral targeting depends on the ratio of transport costs and the discount factor, and that the effect of behavioral data on profits can be positive or negative depending on these factors. The study provides insights into how firms can use behavioral data to improve their marketing strategies and maximize profits.This paper examines how the profitability of behavioral data in mobile marketing depends on firms' time preferences. It shows that the profitability of behavioral targeting can be neutral, positive, or negative, depending on factors such as consumers' transport costs and firms' discount factors. The study finds that when consumers have similar transport costs, it is costly to generate informative data on past purchases, which may lead to a loss of market share. However, if some consumers have very low transport costs, firms may compete fiercely for those consumers, even at close locations, which can reduce profits compared to a situation without behavioral data. The paper also highlights that the precision of behavioral targeting depends on the distribution of transport costs and the discount factor. When transport costs are more heterogeneous, the effect of behavioral pricing on profit is more likely to be negative. The study contributes to the literature on behavior-based price discrimination and mobile geo-targeting by showing that the distribution of transport cost and the discount factor are crucial for predicting the profit and welfare effects of combining behavior-based pricing with mobile geo-targeting. The paper also demonstrates that the profitability of behavioral data may depend on firms' time preferences, and that firms may choose first-period prices to obtain more precise information. The results show that the profitability of behavioral targeting depends on the ratio of transport costs and the discount factor, and that the effect of behavioral data on profits can be positive or negative depending on these factors. The study provides insights into how firms can use behavioral data to improve their marketing strategies and maximize profits.