Today, different customer’s willingness to pay alongside the maturity of data analytics has made it possible to offer segmented prices and gain maximum consumer surplus. Price segmentation is defined as a strategy in which prices are free to adjust based on the "fences" of time of purchase, place of purchase, customer characteristics, product characteristics, etc. Since strategic pricing policies are not practically possible for every single customer, each of them falls into one section. Price segmentation is based on key concepts such as "willingness to pay" and "consumer surplus". However, it also causes "price unfairness perception". Since such feeling is triggered when people compare their deal to another’s, approaches to "make difference in transactions" would be helpful. This study aims to investigate the perceived fairness in three price segmentation methods. The present research is practical in terms of purpose and is descriptive survey research in terms of research method. The statistical population of the research is 384 customers of Tehran's online stores. Data collection was done by questionnaire and data analysis by one-way analysis of variance and least significant difference method using SPSS software. Cronbach's alpha was used to assess the validity of the questionnaires. Findings indicate that the method based on "customer characteristics", has the least level of perceived fairness. The "product bundle" method has increased perceived fairness, but not much, and price segmentation based on "product features" results in the highest perceived fairness.
