Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/24662
Title: Predicting product prices
Authors: Nagarjuna, GDM
Garg, Shyam Prakash
Keywords: Audi A3;Ford focus;Skoda octavia
Issue Date: 2020
Publisher: Indian Institute of Management Ahmedabad
Abstract: Historically, pricing a product was considered easy to many managers and executives as they simply used to add a margin over their variable costs and sell it in the market. But lately, this method of cost-plus pricing is not working out for them and they are increasingly looking towards value-based pricing, i.e., identifying the value of the product to the customers and set the price that matches the value so that it is not overpriced and under sold or underpriced and money is left on the table. In order to identify how the consumer values our product, we need to understand how much he/she values each feature of the product and combine the value of each of these features and then we can put a put a premium on it for the synergy we may have achieved in the process of combining these features. In this project we aim to identify the value attached to features in a product category and try to understand which are more important than others so that a new product when launched can be priced right. Another major challenge faced by companies across the world is how much to produce and this is particularly true when your product is a seasonal good which makes it much more difficult to predict accurately and maintain a lean supply chain. Companies hire temporary workforces or outsource when their product sales have such fluctuations across the year and it becomes extremely important to be able to identify what their sales are going to be for the next quarter not only to manage their supply chain but also to keep their shareholders informed. In the second part of this project we aim to understand and analyze the seasonality and trend of some products in a product category and subsequently predict the sales for the next quarter.
URI: http://hdl.handle.net/11718/24662
Appears in Collections:Student Projects

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