Claims:We claim:
1) The system and method for price optimization by using big data technology is comprising of the contents such as;
a. The web application for price optimization in retail;
b. Any open-source framework like Hadoop for data analysis which stored data on big data; and
c. Any high definition computing device
2) The system claimed in claim 1 wherein, provide retailers the information related to customer’s data for analyse.
3) The system claimed in claim 1 wherein, is used to identify behaviour and elasticity with respect to price change.
, Description:FIELD OF INVENTION
The present invention discloses the system and method for price optimization in retail by using big data technology. The invention is related to retail sector, which are now well developed due to upcoming technologies. Big data is one of the best technologies which can be effectively used for retailing.
BACKGROUND OF INVENTION
Price optimization in retail involves the use of demand modelling and 'what-if' analysis to estimate the impact of pricing on sales, performance, and then fixing a price that works best for the objectives of retail business.
Price optimization is the use of mathematical analysis by a company to determine how customers will respond to different prices for its products and services through different channels. It is also used to determine the prices that the company determines will best meet its objectives such as maximizing operating profit.
Price optimization is necessary in the case where company want to link its business volume with profits and more importantly, if it wants to increase profits by keeping the same levels of customer retention. Price optimization has become increasingly important because sales of personal lines of business have become very competitive.
Currently, price optimization is related to data such as qualitative and quantitative. The customer’s data has to collect first and then it has to check which value actually means to customers. These data should be analysed carefully. And on the basis of that pricing should be done.
The present invention gives the solution to above problem.
The present invention discloses the system and method for price optimization in retail by using big data technology. Big data analytics can be used to analyse customer data and identify behaviour and elasticity with respect to price change. Thus, the pricing of products can be customized for narrower markets like individual states, market segments, and even stores.
OBJECTS OF THE INVENTION
The main object of the invention is that, price optimization in retail by using big data technology.
Another object of the invention is that, retailers use big data to analyse customer data.
Other object of the invention is that, retailers use big data to identify behaviour and elasticity with respect to price change.
SUMMARY OF THE INVENTION
The present invention discloses the system and method for price optimization in retail by using big data technology. The web application for price optimization in retail is created. User/retail team member open the web application. Big data technology help retailer by providing information about customer’s data and helps in identify behaviour and elasticity with respect to price change. With help of this retail team member can analyse data and fix the value of price of item.
BRIEF DESCRIPTION OF DRAWINGS
Fig 1 shows flowchart of the system and method for this invention.
DETAILED DESCRIPTION OF THE INVENTION
The present invention discloses the system and method for price optimization in retail by using big data technology.
Big data is the vast volume of structured or unstructured data that is readily available at our collective fingertips. These large data sets are analysed to reveal previously unknown patterns and provide insights into businesses, human behaviour, and more. Big data analysis is used for everything from financial services, to gauging someone’s productivity, to even monitoring the weather.
The key feature of big data is to analyse, understand and interpret it in order to give user a full a picture as possible about the price optimization.
The system and method for price optimization by using big data technology is comprising of the contents such as;
Any high definition computing device
The web application of price optimization in retail
Any open-source framework like Hadoop for data analysis which stored data on big data
Big data consists of five stages such as-
Data sources- web and social media, machines, Sensing, IoT
Content format – structured, semi-structured, unstructured data
Data stores – document oriented, column oriented, graph database, key value
Data Staging – cleaning, transform and normalization
Data processing – Batch and real time
The system and method for price optimization in retail by using big data technology is comprising of the following steps such as;
Step 1) The web application for price optimization in retail is created
Step 2) User/retail team member open the web application
Step 3) Big data technology provides information such as customer’s data and identify behaviour and elasticity with respect to price change
Step 4) User/retail team member can see the information provided by big data technology
Step 5) User/retail team member analyse the data and fix the value of item
The role of big data technology in invention application is as follows –
Big data technology help retailer by providing information about customer’s data and helps in identify behaviour and elasticity with respect to price change. With help of this retail team member can analyse data and fix the value of price of item. Big data gives businesses an advantage when pricing products. Consistently monitoring relevant search words can enable companies to forecast trends before they happen. Retailers can prepare new products and can anticipate an effective dynamic pricing strategy.
Use of big data is looking promising in the retail field and it can become a standard in the future.
The invention application has some advantages for the invented system, such as;
By using this invention retailers can experience sales growth. They can start selling more since customers consider their prices optimal.
The predictable and scalable success of pricing decisions- when the price is managed right, retailers always know the reason behind it and will be able to repeat the success.
As the amount of varieties of data is soaring, the algorithms which process it are becoming increasingly sophisticated, while computational capacities are getting more rapid. As a result, retailers make pricing decisions quicker and change price more often.