Retail analytics: Definition, types, benefits, use cases

Retail Analytics

There's a historic shift happening in the way consumers shop. Today's consumers can easily compare prices, research products and make purchase decisions that align with their lifestyle. No matter how they shop, customers expect a personalised shopping experience, hassle-free order fulfilment and returns and responsive customer service across multiple channels. Retail analytics allow retailers to meet these growing demands.

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What is retail analytics?

Retail analytics involves using software to collect, analyse and report data from physical and digital sources to give retailers insight into customer behaviour and shopping trends.

Retail analytics can also be used to improve pricing, inventory, marketing, merchandising and store operations decisions. Additionally, they can measure customer loyalty, identify purchasing patterns, predict demand and optimise store layouts.

Retail analytics uses many data sources, including point-of-sale (POS) systems, in-store video feeds and systems that track individual customer purchases and service histories. It sometimes incorporates AI and machine learning to help predict trends, suggest offers and provide the basis for pricing and inventory allocation decisions.

For retailers, retail analytics can help them make sense of common sales metrics such as gross margin, sales per square foot, and average transaction value (ATV); customer metrics such as customer retention rate, customer lifetime value (CLV), and customer acquisition cost (CAC); inventory metrics like turnover rate, sell-through rate and stock-out rate; and marketing and operations metrics such as click-through-rate (CTR) and conversion rate.

Retail analytics tools

Retail analytics relies on several different tools to capture and analyse data across customer touchpoints. Some of the most common tools used are:

Point-of-sale systems (POS). Retailers use POS to track and manage customer transactions. They provide customer purchase data and can generate sales and customer trend reports.
Customer relationship management (CRM) software. Retailers use CRM software to track customer interactions, retain data about individual customers and identify potential sales marketing and customer service opportunities based on that information.
Business intelligence tools. Retailers use BI tools to synthesise information gleaned from large volumes and different data sets to track key performance indicators such as customer loyalty, inventory turns, sell-through rate and days on hand.
Inventory management systems. Retailers use inventory management software to track items in stock, monitor inventory levels in warehouses and distribution centres, and create demand forecasts. The software helps retailers identify optimal locations for storing specific items to minimise transportation expenses and ensure that goods are available to meet customer demand.
Predictive analytics. Predictive analytics uses data from prior transactions, communications, and other actions to predict future trends and behaviours.

Benefits of retail analytics

Retail analytics helps retailers accurately understand and adapt to changing consumer preferences, supply chain disruptions, labour market forces and more. Here are some of the key benefits of implementing retail analytics:

Boost customer engagement

Retail data analytics can re-engage customers close to churning by identifying under-engaged customer segments who are likely to increase their spending with additional personalisation and outreach efforts. Analytics can also identify where wallet share is lost, where that spending is being directed instead and the factors driving consumers to take their business elsewhere. Lastly, analytics can identify behavioural markers that indicate waning engagement.

Optimise pricing and increase profitability

Retail analytics considers complex factors such as the level of demand in individual customer segments, differences in consumer shopping behaviour across channels, and competitive intensity. Analysis of broad data sets can yield valuable insights impossible to see without a complete view across multiple data sources, enabling retailers to create a sustainable strategic framework for price-setting in the long term.

Optimise inventory management and the supply chain

Retail analytics can improve inventory management practices by analysing large data sets from multiple sales and inventory systems to accurately identify the items most likely to go out of stock or be overstocked. Real-time analytics can also provide a comprehensive view of transportation and supplier networks, making it easy to identify ways to trim costs and streamline operations.

Identifying trends

Retail analytics can help shape how and when retailers should contact customers and help shape the product development process. Additionally, it can help stores iterate the in-store experience by experimenting with layout, resource allocation and even how lightly (or heavily) to staff bricks-and-mortar locations. Finally, simple market research allows companies to stay even with or ahead of the competition.

Improving personalisation

Retail helps retailers understand their customers’ preferences and thus capture more demand than their competitors. Retailers can use whatever insights are available to them to segment customers and study them with greater detail. This can give retailers more visibility into how to stock their stores and who to connect with to maximise the appeal of current product lines.

Retail analytics has become a cornerstone for businesses seeking to thrive in an increasingly competitive and data-driven marketplace. By leveraging the power of retail technology solutions, retailers can gain deeper insights into customer behaviour, optimise inventory management and improve sales and marketing strategies.