Profiting from Big Data in Retail

The retail industry is leveraging data more and more, as making strategic decisions requires a thorough analysis of patterns and trends. Utilising big data analytics can be beneficial for retail leaders, as it gives them the tools to make sense of the vast amounts of data they collect on customers’ buying habits, market developments, and other related information.

With the ever-increasing proliferation of “big data” (data with higher variety, volume, and velocity; the “three V’s”), the importance of quality analysis is growing. In this article, we will explore the value of data, its specialised applications, and its relevance in the retail industry.

Retail and the Value of Data

When deciding on stock, suppliers, locations, physical layout, virtual storefronts and customer base, retail executives need to have access to reliable data in order to make informed decisions. Doing so will result in increased profits, reduced costs and a happy, loyal customer base.

Many stores and companies collect customer data in order to optimise their services. Gathering customer feedback can provide valuable insights into their thoughts on the store’s range of products, layout, and any additional services offered. For example, if a store believes that customers prefer not to be disturbed by employees while they shop, yet customer surveys reveal the opposite, failure to act on this knowledge could give the impression of substandard service.

To What End May Big Data Be Used in the Retail Sector?

The recently published Works blog article outlines the various use cases of predictive analytics in retail, highlighting how organisations can gain a competitive advantage in the market. A few examples of how to put data analytics findings to use are provided in the article.

Upgrade Customer Satisfaction. The use of big data in retail operations is extremely beneficial in improving the customer experience (CX). By tracking customer behaviour both in-store and online, organisations are able to gain valuable insight into their customers’ preferences and create specific offerings that cater to them. To learn about the significance of CX, particularly in the context of the pandemic, please watch the video below.

Tune up your advertising. By analysing customer data, businesses such as sports goods outlets can use individual consumer feedback to enhance the experiences of smaller customer groups in their marketing initiatives. For example, they may be able to identify that female bikers are likely to respond positively to certain offers, which can then be used to create targeted content for email newsletters, social media postings, and more.

Find a new price. The Works‘ blog post states that Predictive Price Analytics takes into account previous product pricing, customer demand, inventory levels, competitor pricing and margins to determine the optimal price for each item. This analysis is designed to maximise both profits and sales.

Supervise stock levels. Having the ability to balance conflicting priorities is also beneficial when managing inventory. If inventory is inadequate, customers may opt to wait for the product. If inventory is excessive, the store may need to reduce prices in order to sell the surplus. Quantitative methods such as predictive analytics may be employed to forecast sales.

Implementations of Big Data for Shopping

Uses of Big Data in Shopping Businesses should consider data management at all stages of the process, from collection to storage to analysis of outcomes. To aid with this, please find some useful resources below.

Retail point-of-sale. Point-of-sale (POS) systems are commonplace in retail, and cash registers are a familiar sight. However, modern systems offer more than just the ability to process payments; they provide leaders with valuable insights into metrics such as client count, basket size, sales patterns, profit margins and more, thanks to their reporting capabilities. This data can be used to inform stock planning, employee scheduling and vendor relations administration.

Statistical analysis in advertising. The success of a store’s social media, online ad, and email campaigns can be accurately measured using various metrics. Open rates, average engagement time, and clicks are some of the important data that can be obtained from newsletter systems. Professionals working in the retail sector can gain valuable insight by analysing the most effective subject lines, topics, and messages from their customers.

Analytics of pedestrian flow. Analysis of customer footfall, including the number of customers at various times, days, weeks, months and years, as well as the duration of their visits to specific displays, can be extremely beneficial for merchants in terms of understanding consumer behaviour and preferences. This data can be used to inform staffing decisions and product decisions.

By utilising data science and analytics, companies can gain a more holistic understanding of their operations by combining data from various sources. This provides invaluable insights into the retail sector.

Retail’s Bright Prospects for the Future

We have observed first-hand the increase in data volume and speed. Computers’ capacity to manage this data has evolved accordingly. Solutions based on Artificial Intelligence (AI) and Machine Learning (ML) are continually improving their ability to extract meaningful insights from data. These technologies are aiding businesses to not only review what has occurred, but also anticipate how to position themselves for future success. Predictive analytics in retail will continue to be advanced through further development of these skills.

Having access to data and analytics can enable businesses to provide their customers with an optimised experience. For example, if a company can accurately predict a customer’s needs, they can be proactive in providing those requirements, potentially at a discounted rate. Additionally, businesses can more effectively respond to their competitors’ marketing campaigns when they have an understanding of when those campaigns are likely to be launched. Accurate forecasting and the effective utilisation of this data is critical for the future of retail.

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