Research On Pricing Strategy Of Big Data Transactions Based On Auction Theory

With the rapid development of information economy in today's society and the emergence of various new Internet technologies,big data has become the core resource that every industry attaches importance to.With the development of government departments,Internet-related enterprises,and a large number of financial institutions,a huge amount of data information has been generated.The acquisition of these massive data has been relatively mature.The key is how to conduct in-depth analysis and mining,and then create huge profit value.With the development of the times,a new business model of big data transaction has emerged,which promotes the arrival of the "DT"(data technology)era.In the DT era,massive data has been incorporated into the company's important assets and become an indispensable factor of production,resulting in large data trading platforms and data exchanges.Based on the significant difference between big data and ordinary commodities,the traditional price theory can not meet its pricing requirements,and lack of a large number of historical data experience to provide reference,so there is no unified and perfect trading mode and pricing mechanism at this stage.This paper summarizes the background and current situation of big data transactions,combs the domestic and foreign literature on big data transactions,pricing of information products and auction-related research,and introduces the relevant content of auction theory,including the basic concepts of auction,auction methods according to different standards and three basic auction models.Then it focuses on the characteristics of big data commodities and trading participants,analyses the basic trading process of big data,and explores the existing problems.Based on the data trading of large data trading platforms at home and abroad,it concludes that different trading modes can be classified according to the structured degree of data and the transfer of property rights in the current market.Finally,it collates the common large data pricing transactions of major trading platforms.Easy strategy.After studying many trading platforms in the market,it is found that sellers are in the leading position,unilaterally deciding the trading mode and price,keeping the transaction price of big data commodities at a high level,reducing the number of buyers in the data trading market,and making it difficult to increase the growth rate of the transaction market scale.This paper combines the pricing of information products and traditional auction model to analyze the pricing strategy of big data transaction.After the game analysis of big data buyers,sellers and trading platforms,it is concluded that sealed auction should be implemented from the perspective of long-term development planning.Then,under the assumption of common value model,the paper analyses the bidding under sealed auction of primary price and sealed auction of secondary price.The expected earnings and quotation of the bidder.The information mismatch between the two parties in the actual transaction has transaction trust risk.By establishing the influencing factors model of auction transaction price and using regression equation to analyze the influencing factors of auction transaction price,it is found that the seller's credit has a significant impact on auction transaction price.Therefore,this paper introduces a credit scoring mechanism in the basic auction model.The scoring results from the scale of the delivery data and the historical auction records.The evaluation and the reliability of the data are determined by comprehensive information.The score represents the seller's credit.Under the new auction model,the bidder's quotation,expected revenue and the demander's payment price are studied again.The simulation results with MATLAB software show that the scoring mechanism has a significant impact on the transaction pricing.The smaller the score,the better the credit,the bidder will have a greater advantage in bidding.The more bidders,the better for the demander,so the demander of the data should attract as many bidders as possible to participate in the bidding before bidding,and should not give the same score to the bidder;bidders should quote according to the true evaluation in the auction,and constantly improve their data qualifications and users' praise to improve their credibility,so that both parties can get more benefits.This paper combines the pricing theory of information products with the traditional auction model to price big data transactions,adds credit factors to improve the pricing model,and adds the auction model of scoring mechanism to help reduce the information asymmetry between the two sides.On the one hand,it improves the buyer's voice,makes the data buyer have full choice,saves the buyer's cost to a certain extent,and makes the user rules of big data buyer.On the other hand,it helps to form a standardized data trading market and price.The scoring mechanism can eliminate the fittest and make the transaction more scientific and standardized,because only by improving the data quality can bidders improve their own evaluation and promote the transaction.