Machine Learning in Retail Sector : Use Cases




Use Cases


Recommendation engines : Data filtering and they use collaborate or content based filtering.The retailers tend to use recommendation engines as one of the main leverages on the customers opinion.


Market basket analysis: Its basically Traditional tool of data analysis. Data mining analysis will be applicable here. This process mainly depends on the organization of a considerable amount of data collected via customers transactions.


Warranty analytics : Warranty analytics comes in picture of retail as a tool of warranty claims monitoring, detection of fraudulent activity, reducing costs and increasing quality. Basically here data and text mining methods will be used for identification of required resources.


Price Optimization : Having a right price both for the customer and Using the model of a real-time optimization the retailers have an opportunity to attract the customers, to retain the attention and to realize personal pricing schemes.


Inventory management: Inventory, as it is, concerns stocking goods for their future use.


Location of new stores: Data science proves to be extremely efficient about the issue of the new store’s location. Usually, to make such a decision a great deal of data analysis is to be done.


Sentiment analysis : Customer sentiment analysis is not a brand-new tool in industry. However, since the active implementation of data science, it has become less expensive and time-consuming. Machine learning algorithms provide the basis for sentiment analysis.


Merchandising : Goal to increase of sales and promotion of the product.


Security : If any fraud detection activities security comes in picture of any retail industry. The detection of fraud and fraud rings is a challenging activity of a reliable retailer. Hence security matters here with all aspects.


Price Optimization in Detail

Having a right price both for the customer and the retailer is a significant advantage brought by the optimization mechanisms.
The price formation process depends not only on the costs to produce an item but on the wallet of a typical customer and the competitors’ offers.
The tools for data analysis bring this issue to a new level of its approaching.

price optimization for retail — which has its own particularities — and how retailers can take advantage of the tremendous power of Machine Learning technology to build effective pricing solutions.


Steps to Get Your Data Ready For Price Optimization


Few points to be taken care


What price should we set if we want to make the sale in less than a week?


What is the fair price of this product, given the current state of the market, the period of the year, the competition, or the fact that it is a rare product?


price optimization uses data analysis techniques to obtain two main objectives:


1)Understanding how customers will react to different pricing strategies for products and services, i.e., understanding the elasticity of the demand.


2) Finding the best prices for a given company, considering its goals.

price optimization allow retailers to consider factors such as:


Competition

Weather
Season
Special events / holidays
Operating costs
Warehouse information

To Determine

The initial price
The best price
The discount price
The promotional price

Happy Reading:-)

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