Big Data Analytics in Retail Industry

Introduction:

In recent times, corporate around the world have started adopting data analytics as a powerful tool to help them optimize various aspects of their business. Retail is no different. Uncertain economic conditions, expensive real estate, narrow margins, and shifting customers – all these factors are pushing retailers to find innovative ways to retain customers and improve bottom line.

There are many ways in which retailers can apply analytics in their business. Some of the world’s most successful retailers have aggressively adopted and exploited analytics to help them gain continuing competitive advantage.

 Application of Analytics in Retail:

Successful Retail companies have applied analytics to various parts of their business.  An overview of the important areas where  analytics can add value to the retailers is described below:

  •  Market Basket Analysis:   Using market basket analysis, retails get a window into customers’ purchasing behaviour. This Analysis can help the retailers answer the following questions:
    • What products are purchased together ?
    • What is the buying behaviour of the most profitable customers ?
    • Which are the most profitable basket ?
  • Store Layout Planning:  Store layout planning is vital for boosting customer satisfaction and driving sales. Data can help retailers create effective store layouts by answering several questions:
    • Which departments will attract maximum traffic ?
    • Which departments should be located close to each other ?
    • How customers navigate the store ?

 

  • Sales Forecasting :   Sales forecasting is the process of a company predicting what its future sales will be. This forecast is done for a particular period of a time in the near future, usually the next fiscal year. Accurate sales forecasting enables a company to make informed business decisions

 

  • Demand  Analysis:  Research into the desire of  consumers for a  particular product. Demand analysis is used to identify who wants to buy a given product, how much they are likely to pay for it, how many units they might  purchase, and other factors that can be used to determine product design, selling cost. and advertising strategy for a product. On other words, we can say that which products have high demand according with the customers

 

  • Identifying Customer:  This is also important in data analytics in retail because choosing which customers would likely desire a certain product, data analytics is the best way to go about it. Because of this, most retailers rely so much on recommendation engine technology online, data gotten via transactional records and loyalty programs online and offline. Companies like Amazon might not be ready ship products straight to the customer’s before they order; they are looking in that direction. Individual geographic areas depend on demographics that they have on their customers which imply that demand is forecast. Therefore, it means that when they get orders, they are able to fulfill them more efficiently and quickly while data gotten depicted how customers make contact with retailers is used for deciding which would be the best path in getting their attention on a certain product or promotion.

 

  • Customer’s Sentiment AnalysisCustomer sentiment analysis is a method of processing information, generally in text format and often from social media sources, to determine customer opinions and responses. Analysis of the data allows organizations to assess whether customer reaction to a new product was positive or negative, or whether owners of a product are experiencing major technical difficulties.

 

Conclusion:

The world of retail industry  has drastically changed over the past decades and retailers of all sizes are  incorporating analytics into their operations. Retail analytics can help you gain actionable insights hidden into the wealth of data available with you.

One comment on “Big Data Analytics in Retail Industry

  1. What a fantastic read on Data Science. This has helped me understand a lot in Data Science course. Please keep sharing similar write ups on Data Science.

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