Four ways data analytics can boost retail performance

Dan Hartveld
19 September, 22

Data is the lifeblood of all organisations and it’s growing at an exponential rate – it’s predicted that the amount of data generated each second in the financial industry alone will grow 700% in 2022. But a recent Red Ant survey revealed that not all retailers know how to capitalise on the data they already have to build competitive advantage and boost revenue. In fact, almost half of retailers (49%) claim their data strategy is clear but not widely understood across the organisation and 39% lack confidence in the quality of their data.

But the answer for retailers to outperform their competitors and improve sales and customer retention could lie in retail data analytics, in collecting, studying and utilising their customer and product data to create actionable insights.

What is retail data analytics? 

Retail data analytics is the process of using accumulated data to optimise areas of a retail operation. This includes understanding customers’ buying needs so that they can focus on areas that have high demand. It is the practice of using digital technologies such as AI and machine learning to analyse data in order to make intelligent and meaningful business decisions. Companies that utilise retail data analytics to improve their business tend to outperform their competitors as they gain a single customer view of the entire shopping journey.

In these uncertain economic times, retailers need to ensure that their in-store and online operations perform optimally, as well as sharing all customer data to maximise retail success. The essential foundation for harnessing all retail technology operations – an omnichannel retail platform – brings essential retail apps together with a retailer’s existing systems, content, and data into a single colleague hub and is informed by retail data analytics. Once everything is in one place, the platform empowers store associates with the right information on customers and products to deliver next level customer service.

Four ways to transform retail operations with retail data analytics

Retailers’ challenges are many and varied and span all retail processes, from capturing customers’ details to managing stock levels, inspiring, and retaining store associates, identifying opportunities for cross-selling and up-selling, and keeping customers loyal with personalised, rapid and efficient service.

There are four key ways in which retail data analytics can significantly improve retail operations and increase sales:

  • Provides customer behaviour insights

Collecting data and analysing the results is a great opportunity to understand how your customers are shopping and improve your customer experience based on their behaviour. For example, retailers might find that their shoppers visit a store to browse their products but leave before purchasing to buy online. The retailer may choose to take action to unify their online and offline shopping channels, focus on providing great in-store customer service and consider ways to reward the store associates who helped close the sale before they bought online.

  • Promotes long-term loyalty

Retail data analytics can also be used on a one-to-one level. Accumulated data gathered from a single customer means that store associates can view a selected customer’s purchase history, wish list, returns and even notes created about them including product preferences and vertical-specific information such as allergies or a specific shade of skin colour for cosmetics. This provides a truly personalised experience with the customer feeling the store associate has handheld them through the purchase. With relevant data recommendations, store associates can upsell effectively while ensuring every product is suited to the shopper. This results in more repeat purchases with the retailer and avoids them straying to alternative brands.

  • Improves return on investment

Retail data analytics can also help retailers determine and provide the best promotions for their customers. Understanding the most popular products, customer demographics and recommendations means retailers can utilise their past campaign performances as well as current insights to create promotional offers that are personal to their customers and are more likely to encourage a sale.

  • Managing in-store operations

Retail data analytics can also improve everyday in-store operations. They can make predictions to help with inventory management and streamline back-room processes. Furthermore, retail data analytics can help pinpoint product popularity, stock levels, voucher redemption, speed of service, average purchase value and more, allowing you to efficiently manage in-store activity, make real-time changes and improve overall retail performance.

It’s all about omnichannel

Retail data analytics will only be effective if retailers have a truly omnichannel approach and data flows freely across all channels giving a single view of the customer. According to McKinsey, this means, “Next-generation retail architecture is fully omnichannel, powered by data, and highly modular.” Retail data analytics can underpin and empower retail operations for the long term by enabling intelligent data-driven decisions that maximise sales, deliver exceptional customer service and drive operational performance.

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