Companies have been using various methods to ensure their sustainability and to increase their profit. Demand estimation can be defined as a process that involves coming up with an estimate of the amount of end use demand for a product or service. Companies use demand estimation techniques for various reasons. One of the reasons that businesses use demand estimation is to determining the production amount in a certain period of time and accordingly keep the inventory level under control. An additional consideration is to assist with pricing. Decision maker's uses various techniques for demand estimation. One of the techniques is using the earlier period data and the other is utilizing the test markets which are similar to larger targeted markets. In this paper, we resort to Bayesian approach for demand estimation and utilized the textile products export data which belongs to years between 2002 and 2014. In Bayesian analysis the new information is combined with the previously available information. At this point the prior information (distribution) corresponds to the historical data or the subjective thought of the decision maker about the unknown parameter of the involved process. The performance of the following updates depends on the prior information therefore the determination of prior information is significant. In this research to obtain the future demand level for the previously mentioned data we utilize the conjugate prior families to obtain the posterior distribution. (C) 2015 The Authors. Published by Elsevier B.V.