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How can you leverage big data to improve customer experience in retail?

Big Data Analytics and Artificial Intelligence play a considerable role in transforming the retail industry and unlocking hidden business potential.

Big data has been a buzzword in the business world for years now, but it's finally time to figure out what it means. Data can be collected from many different sources and analysed using analytics to help businesses make informed decisions. This article discusses how big data can accelerate business growth by revealing hidden insights that will lead to a better customer experience.

In the retail industry, Big Data Analytics can improve operational efficiencies across several different dimensions. For example, retailers are increasingly using data to optimise their inventory management and distribution processes to reduce costs and increase sales. The ability to generate insights from stored customer profiles enables companies like Walmart or Amazon to offer personalised product recommendations that drive up purchase rates by around 15%.

How is Big Data applied in the Retail Industry?

Price Optimisation

Retailers use Data Analytics to uncover opportunities for price optimisation. They can do this by monitoring their competitors' prices and seeing which ones are trending lower or higher than they charge on the same product. By detecting when an item's sale is nearing its end date, retailers will know that it isn't worth lowering the price of that particular item because it may not sell before it goes out of stock.

To increase sales in specific categories, such as summer clothing lines during winter months, retailers may strategically change a few pricing points throughout the year so that customers can access these items at more opportune times.

Making Strategic Decisions

Decisions related to opening up new retail outlets, daily business performance monitoring, and other aspects of the day-to-day operation are easy with Big Data Analytics. You can also use artificial intelligence for short term decisions like promotional offers or displaying products in a particular manner at various stores.

Personalising Customer Experience

Analysing customer's data helps in customising the discounts/offers for unique customers. This data is related to purchase history, search history, average bill value, frequency of visit to the retail stores - customised SMSs and emails related to offers or generated discounts through big data analytics.

Concepts of Big Data in Retail

Let's explore some Big Data Analytics concepts:

Recommender Systems

Amazon generated 29 percent of its sales through its recommendation engine, which analyses more than 150 million accounts - this is a significant contributor to Amazon's profits every year.

When you buy from an e-commerce platform or online retail, recommendations will frequently come up for other items that may go well with your purchase. Things typically bought together by people who have purchased the product you are buying.

Recommender Systems are a popular tool used by ML-driven websites like Amazon, Flipkart, and Bigbasket. These sites use Data Analytics to recommend items for you based on your past searches and purchases.

Predictive Analytics

One of the most common ways people pay for groceries and other retail-related items is using a credit card. However, recent studies have shown that there's also been a significant increase in credit card fraud.

One retailer, Amazon, was able to reduce 50 percent of their credit card fraud by using predictive analytics. Amazon has worked with predictive analytics programs from the outset and has completed extensive success using them over time and incorporating machine learning technologies into their investment strategy.

The central purpose of every business is to make a profit. Retailers make many decisions related to inventory and how much quantity they should order from the manufacturer. They assess the market demand for various products and consider customer responsiveness as well.

Therefore retailers often employ predictive analytics when forecasting future demand and growth to determine profits with more grace and accuracy.

Operational Analytics and Supply Chain Management

From where to open a retail store, to which stores have the highest customer footfalls, and which ones are performing well - this type of operational decision can be solved by analysing past sales data. Apache Hadoop is being used in many cases to analyse millions of sales records for insights that might not be immediately apparent without extensive volume analysis.

You may find that implementing one or all three concepts will give you more insight into your customers, increase customer satisfaction and loyalty, improve efficiency in sales and marketing processes as well as better differentiate yourself from competitors. The key is to build a personalised experience for each individual by understanding what they are looking for at every stage of their purchase journey, as well as predicting what else might interest them.