Technologists, ecommerce specialists, and shoppers are all equally excited about the possibilities that utilizing big data in retail can unlock for them. In this article, we’ll look at the power and benefits of big data analytics in retail, as well as applications and examples of big data in the retail industry.
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Introduction to Big Data Analytics in The Retail Industry
Customers want a personalized, real experience. They want online channels and brick-and-mortar stores to meet their needs and provide an engaging experience. If they can’t easily make a purchase or experience delays, they are spoilt for choice. There are hundreds of other retailers they choose from at a moment’s notice. Retail merchants that want to increase sales and customer satisfaction.
Retail analytics is the process of using big data to optimize pricing and the supply chain and even to improve and build customer loyalty. Large volumes of data are being collected every day. When we respond to an online ad, redeem a coupon, click on different items in an online store, or add something to our carts, it says something about what we like or dislike. Big data describes the large volume of data that is used to reveal patterns, associations, preferences, and trends. For retail companies that use big data, it means gaining the ability to understand consumer shopping habits. When they have more information, they can tailor their customer recommendations, inventory, and marketing efforts to create personalized shopping experiences and improve forecasting trends, and make strategic decisions based on the analyses.
Benefits Of Big Data and Knowledge Management In Retail
There are several ways big data can be collected in the retail industry, both online and offline. Loyalty cards and programs that run via apps or physical cards can provide rich insights about preferences, including which promotions customers respond to, which items they buy regularly and how often they make purchases. It can also be collected through credit card transactions, IP addresses, user log-ins and behaviors on the site or interactions with social media and other marketing efforts.
Retail businesses can collect this data and use market insights to analyze their customers’ searches and spending. This information can be used to predict future spending and make better recommendations. Here are a few of the benefits associated with big data in retail:
Optimized Supply Chain
In the Amazon age, customers are used to same-day delivery and have very little patience with delays. Using big data analytics, retailers can quickly and easily determine when and how, and where deliveries are being delayed. They can also hone in on issues related to vendors, stock, and warehousing. For example, if they pick up that specific items are constantly running out but remain in high demand during the festive season, they can adjust their ordering to ensure that enough stock is available at all times. If they pick up that some vendors have longer lead times, which lead to delays, they can put together forecasts and determine when to place orders to meet seasonal demand.
Predicting Spending
Amazon uses customer data to make recommendations for you based on your past searches and purchases. They generate more than a third of their sales through their highly intelligent recommendations engine. This engine regularly and automatically analyzes more than 150 million accounts, which can boost profits. Retailers can also pick up emerging trends to find out which items they should be stocking but don’t have yet. They may pick up that customers are looking for a particular item they saw reviewed on a popular TV show or that there is seasonal demand for specific items.
Personalizing Customer Experience
Retailers can use big data to create opportunities to provide improved customer experiences. Costco uses transaction data collection to keep customers satisfied and informed. There was a high-profile incident where a fruit packing company warned the retailer about the possibility of listeria contamination on their peaches and plums. Costco was able to mitigate and limit the fallout by emailing customers who had bought those fruits instead of everyone on their email list. Retailers can also learn about customer preferences and provide personalized recommendations or coupons via email to increase spending.
Forecasting Demand in Retail
Retailers can also go beyond big data by analyzing social media and web browsing trends using AI. This can predict the next big thing in retail. Brands like Walgreens and Pantene worked with a fascinating data point: the weather. They partnered with the Weather Channel to account for weather patterns to customize product recommendations for consumers. Walgreens and Pantene anticipated that high humidity would affect women’s shopping habits and the sale of anti-frizz products, so they began serving up ads and in-store promotions in order to drive sales. The purchase of Pantene products at Walgreens increased by ten percent over just two months, while Walgreens saw a 4% lift in hair care sales during the same period. Retail forecasting and retail projections are used to allocate resources and even stock up in a smarter way throughout the year. This can avoid issues like overstocking. When retailers buy products that aren’t good sellers, these products take up space on retail shelves and in warehouses, which costs them money without making a profit. By analyzing demand, over- and understocking can be avoided.
Customer Journey Analytics
The customer journey doesn’t always follow a predictive pattern or line. Customers may visit numerous stores, interact with various touchpoints, and read a variety of materials before making a final decision. The only way to speed up or manipulate that journey to close a sale quickly is to create better experiences by using big data. Analytics can answer questions about where customers are looking for information, where they are dropping off, and which methods are the most effective to reach them and convince them to make a purchase.
Expansion
Retail chains and businesses can use analytics to understand the differences in demand for their product across new territories and various geographic locations. By analyzing consumer trends and spending analytics, retailers can use the data to better service customers in new regions and stock products with greater efficiency and accuracy.
Now you know the benefits of big data in retail, how will you use it in your own business?
