MARKET PRICE AND DATA ANALYSIS

Transcription

1 International Journal of Civil Engineering and Technology (IJCIET) Volume 8, Issue 12, December 2017, pp , Article ID: IJCIET_08_12_048 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed MARKET PRICE AND DATA ANALYSIS Department of Computer Science and Engineering and Department of Information Technology Vardhaman College of Engineering, Hyderabad, India ABSTRACT We are experiencing an increased rate of transaction via net. With the rise in number of transactions, we need to handle data carefully. The volume of data associated with these transaction also increases. This throws some statistical challenges in front of the researcher apart from increasing business opportunities for the traders. In this paper we propose a way that finds the optimal price in software industry using a voluminous dataset from Flipkart.com. We will analyze in this paper the cost and demand of each product and ultimately reach the optimal cost. We will be adhering to backward-outlining to estimate such parameters that will help us to reach to our goal in finding the optimal cost. Key words: E-commerce, data analysis, pricing Strategy, quality differentiation, sales force. Cite this Article: Dr. S. Sai Satyanarayana Reddy, Dr. H. Venkateswara Reddy, J. Bheemeshwara Sastry,, Market Price and Data Analysis. International Journal of Civil Engineering and Technology, 8(12), 2017, pp INTRODUCTION The endorsement web based ecommerce which provide the high growth in data which is related to price and demand across various organizations. The existence of data has high growth in e commerce which raises new issues in both econometric and statistical issues. In this paper we come across how to observe and analyze data which evaluates strategy in pricing of customer software industry which makes use of large scale ecommerce data store from flipkart.com. The strategy involved in pricing the customer related software industry involves often in price prejudice aims to determine the customer who are interested to pay high for a product leads to charge then high price. The first and foremost degree of price prejudice is to charge different customer with different prices. Thus to profit from price prejudice as it choose the price optionally. The primary goal of our paper is to use the information to evaluate with most favorable way of price prejudice strategies in a industry experimentally which a rise the problem of editor@iaeme.com

2 Market Price and Data Analysis confessional economic importance. Our primary need is method to convert sales rank reports which examined by flipkart.com. In which gives the report in daily basis of each product it sells and its category based on recent demand. The privacy restrictions of web data is to be respected which impacts more on customer involvement. The systems has become more efficient in handling huge amount of data which continuously keep on increasing which should provide privacy practice for web visitors for shopping. There will be variation in data as well along with the price of product based on demand which in order to meet the estimation of the organization. As Flipkart.com does not accommodate any sort of data about volatile cost of variety of products it sells. As we to demand to figure out the cost incurred from our data in which the profit of the organization is estimated based not only based on the price what we have charged but also on the quantity we had sold within a period as per a unit of product. This approach summarizes the estimations of organizations which play a vital role in meeting the challenges related to statistics that emerges our analysis. 2. RELATED WORK There exists a wide variety of approaches to determine for firming price discriminations as from a view the seller can price differently which depend on the customer which analyze whether the customer as purchased the same product from a market place before which typically offers introductory offers [1,2]. The seller may increase the price of the product with comparing other related products which are been purchased from the same dense which named as bundling [3]. As the seller will choose to apply pure bundling in which the fixed amount of products are which can be sold in a bundle or can be chance of mixed bundling [1,2]. The sales ranks can be associated with the demand of products in a period bases which provides the reports and each and every product which has been sold in its organization both directly or through online with the relative demand on products as well its related products[4]. The primary objective of an organization is that in price discrimination to generate the profits from the product which have been sold in one organization however the price discrimination can balance the profits of firm. For an example two issues which related in introducing the existing product with low quality discriminate the price which includes loss of profits in switching the customer towards with the products with higher quality of product along the rise in cost which leads higher price [5,6]. The data mining way of approach for handling the data of e commerce is been discussed with three algorithm i.e, clustering, prediction and association which leads in profits in consumer customer relationship, marketing sector and analysis on business planning foundation. The transformation of data along with the slow change in data for business people, which come up in designing different models, can be useful for the users. In which indicating the market in transactional services which is achieved by merchant like ebay, Amazon foe their innovative product recommender systems as well platforms [7]. As discussed the experimental data which is collected in order to apply intelligent algorithm on which is applied for conducting compromise the process. The huge volume of data is produced and gathered in which latest techniques of computer is used in order to store, editor@iaeme.com

3 process and manipulate the data as per the requirement of the organization which keeps on tracking to improve business performance [8]. A study which analyze the influence of store perception in online sales as 65% of the users in 2002 often look to offline stores rather than in buying online which indicate distinct cross channel effects towards offline and online retailing. But as of now which leads in comparing since 2002 to till date which come across a drastic change in usages of online shopping systems across the world [9,13,14]. In contempt of increasing flow in customer related data the forum often lack in the ability of using the information. We should have an opportunity of analyzing and collecting the data accordingly with the increase in customer information. Online shopping leads in transmitting customer personal information (like mobile number, card details and shipping address etc) to the server side which should have enough security in maintaining it [10-12]. 3. PROPOSED SYSTEM In this paper we come up with an approach that finds the optimal price in software industry using a voluminous dataset from Flipkart.com to analyze the cost and demand of each product and ultimately leads to optimal cost. We collect data from Flip kart. These data are related to prices of the new software. Apart from prices, these data also identifies the ranking of these software related to their sales in the market. We will be doing analysis on 300 samples. There will information on two kinds of prices, MRP, what price is listed by Flip kart and the sales rank of each items. We will be also collecting information on the release date of the product, and also customer s feedback. Then we will be estimating the variable price of the products using Linear Regression Analysis. This will give us the fluctuation of price with respect to time. Next step of our proposed system, will evaluate the monopoly of quantity and price. In this step we will be using marginal revenue and marginal cost. We will be finding the values of MR and MC. Next we find the variable cost that will help us to find the optimal price using the formula: q j + [{( r i -d i )/w i *}q j ] m ij =0 for i,j= 1,2..n Figure 1 Framework of Optimal price estimation editor@iaeme.com

4 Market Price and Data Analysis 4. CONCLUSIONS The usage of data with the high growth in e commerce services plays a major role in making decisions in the business world. As there will huge growth in data related to ecommerce retailers which helps them predicting user interests as with the change in demand. With the help of this survey we come up with a research in ecommerce services which leads to huge profits in one s organization. The major aim of this paper is to experience with an increased rate of transaction both direct and online and to handle the huge assortment of data in deliberately. As the ultimate challenge to generate a great business world is to handle the huge explosion of data that makes value in profits which makes a better understanding in decision making. With the rise in demands to face the challenges statistically along with the great opportunities in the business environment which propose a way to find price prejudice way of approach that leads in profits within an organization. We analyze the optimal prize with in a software industry using large massive data set from flipkart.com which estimates using such parameters to reach our goal in a finest way. REFERENCES [1] Ghose, A., Huang, K. And Sundararajan, A. (2005). Versions, successive generations and pricing strategies in software markets: Theory and evidence. Working paper, Stern School of Business, New York Univ. [2] Aron, R., Sundararajan, A. And Viswanathan, S. (2006). Intelligent agents in electronic markets for information goods: Customization, preference revelation and pricing. Decision Support Systems [3] Bakos, Y. And Brynjolfsson, E. (1999). Bundling information goods: Pricing, profits and efficiency. Management Sci [4] Chevalier, J. And Goolsbee, A. (2003). Measuring prices and price competition online: Amazon.com and Barnes and Noble.com. Quantitative Marketing and Economics [5] Choudhary, V., Ghose, A., Mukhopadhyay, T. And Rajan, U. (2005). Personalized pricing and quality differentiation. Management Sci [6] Ghose, A., Smith, M. And Telang, R. (2006). Internet exchanges for used books: An empirical analysis of product cannibalization and welfare impact. Information Systems Research. [7] Mustapha Ismail, Mohammed Mansur Ibrahim, Zayyan Mahmoud Sanusiand MuesserNat,"Data Mining in Electronic Commerce: Benefits and Challenges, International.Communications, Network and System Scien-ces,pp: ,December [8] Hasan Al-Sakran,"B2C E-Commerce Fact-Based Negotiation Using Big Data Analytics and Agent-Based Technologies "International Journal of Advanced Computer Science and Applications, Vol. 5, No. 12,pp: 30-37,2014. [9] Gallaugher, J. M. (2002). E-Commerce and the Undulating Distribution Channel. Communications of the ACM, 45 (7), [10] Goersch, D. (2003). Multi-Channel Integration in the Retail of Physical Products. Copenhagen Business School, Copenhagen, Denmark. [11] Gulati, R., & Garino, J. (2000). Get the Right Mix of Bricks and Clicks. Harvard Business Review, 3, editor@iaeme.com

5 [12] Steinfield, C. (2002). Understanding Click and Mortar E-Commerce Approaches: A Conceptual Framework and Research Agenda. Journal of Interactive Advertising, 2 (2). [13] S.Sai Satyanarayana Reddy, S. Nagarjuna Reddy, N. Subba Reddy, Framework for efficient authentication mechanism for genuine user s payload transmission in cybernetic locations in International Journal of Civil Engineering and Technology (IJCIET),8(8), 2017, pp [14] S. Sai Satyanarayana Reddy, G.S. Prasad Reddy, B.Balakirshna, N. Sirisha, Drone control vehicle for prospective protected shipping in International Journal of Civil Engineering and Technology (IJCIET), 8(8), 2017, pp [15] Dr. Vinit Dani and Vanishree Pabalkar, Value Added Services Challenges and Opportunities in India, International Journal of Marketing & Human Resource Management (IJMHRM), Volume 4, Issue 1, 2013, pp [16] Nitin Panwar and Mahim Sagar, Can Value Added Services be a Point of Purchase Differentiator?, International Journal of Management (IJM), Volume 1, Issue 1, 2010, pp , editor@iaeme.com