Instead of guessing what customers like about inventory, layout and service specials, companies can find out what's really on customers' minds.
Data is becoming more important across industries, including retail. Why? Because analyzing patterns and trends is the best way to make critical decisions that drive business success. Big Data Analytics in Retail Settings can help leaders understand the information they collect about customer purchasing habits and preferences, industry trends, and other relevant factors.
Given the increasing use of big data – that is, data with greater variety, volume and velocity (the three Vs) – quality analysis is even more important. In the sections below, we explore the importance of data in the retail sector, how it is used specifically, and the applications that can help.
The importance of data in the retail sector
At the heart of every business are a wide variety of decisions about inventory, suppliers, locations, floor plans, e-commerce and, of course, customers . That's why data is important in retail — it provides critical information needed to inform those decisions, allowing leaders to rely on undeniable facts and trends rather than just guesswork or intuition. These data-driven decisions lead to higher profits, lower costs, and happier, more loyal customers.
One example is shopper insights, collected by a high percentage of retailers and brand manufacturers. Instead of guessing what customers like about inventory, layout and service specials, companies can discover what's really on customers' minds and make decisions based on those insights. Let's say a retailer believes that customers don't like being disturbed by employees while shopping, and research reveals that customers want to be approached. Failure to act on this insight can lead to the impression that a store offers poor service.
How can Big Data be used in retail?
Improve CX. Big data in retail operations is particularly useful for improving customer experience (CX). For example, as companies collect information from transactions and interactions – including from physical stores and online purchases – they learn more about customers' habits and can provide targeted recommendations and services. The following video explains why CX is so important, especially in the wake of the pandemic.
Adjust marketing. The same insights used to create better experiences for individual customers can be used to target small groups of customers in marketing campaigns. For example, through the use of data analytics, a sporting goods store can identify female cyclists as receptive to certain messages. It can create newsletter items, social media posts, and other items to send these messages.
Manage inventory. Inventory is another area that requires the ability to balance multiple considerations. Not enough inventory means customers can take their business elsewhere rather than waiting for an item. Too much means a retailer may have a surplus that they must sell at a discount. Predictive analytics can be used to predict inventory needs.
Big Data Applications in Retail
Big data applications in retail include a variety of solutions. Companies must consider how to effectively manage data throughout the entire process, including collection, retention and analysis. Here are some tools that can help.
POS system. Retailers are very familiar with point-of-sale (POS) systems to record sales. But they can be used for so much more. Modern systems include reporting capabilities that can help leaders understand things like customer counts, basket sizes, sales trends, profit margins, and more. This information is useful for planning inventory, scheduling staff, and managing supplier relationships.
Marketing analysis. Retailers can analyze customer responses to marketing efforts, including social media, online ads and newsletters. For example, newsletter platforms can calculate useful metrics like open rates, average engagement time, and number of clicks. This information can inform retail professionals about which subject lines, topics, and messages people respond to best.
Pedestrian traffic analysis. Foot traffic is another area that can provide valuable data that can help retailers understand customer patterns, including the number of customers at different times of the day, week, month and year, as well as how long they stay. stop at certain displays. This information is useful in making personnel decisions and determining which products to continue selling.
With information from multiple sources, companies can combine reports to develop a sophisticated picture of what is happening across all aspects of their operations. Retail data science and data analytics enable these valuable insights.
The future of retail
As we have seen, the amount and speed of data has increased. To keep pace, the capabilities of computers to process this data have also increased. Artificial intelligence (AI) and machine learning (ML) solutions are getting better at mining data for actionable insights and will continue to get smarter. In addition to being able to summarize past activities and assess what's happening now, these technologies are helping companies predict what's next. These capabilities are being enhanced and will continue to drive the role of predictive analytics in the retail industry.
What will these insights enable companies to do? On the one hand, it will help them make their services more personalized. For example, if a company can predict what a customer will buy, it can offer that to them in advance, perhaps with a special discount. If a company can determine the likelihood of a competitor's next marketing campaign, it can counteract it with its own. The bottom line is that the future of retail is based on relevant predictions and the ability to act on them effectively.
