What is Retail Analytics?

Retail analytics delivers the data behind every transaction and interaction that happens in your store. Metrics like sales per square foot, gross margin, foot traffic, walk-in rate, and comparable sales growth tell the story of your store and help you make profitable decisions. Retail analytics from SAS lets you apply omnichannel analytics to every step of the customer journey for better connections and deeper insights. SAS delivers a strong data strategy, analytical merchandising, and intelligent marketing in an open analytic ecosystem.

Retail analytics is the process of providing analytical data on inventory levels, supply chain movement, consumer demand, sales, etc. that are crucial for making marketing and procurement decisions. The analytics on demand and supply data can be used for maintaining the procurement level and also for taking marketing decisions. Retail analytics gives us detailed customer insights along with insights into the business and processes of the organization with scope and need for improvement.

What Can I Use Retail Analytics For?

There are several excellent retail analytics examples that are relevant to a variety of companies. One of the biggest benefits the field delivers to companies is optimizing their inventory and procurement.

In addition, they can optimize inventory management to emphasize products customers need, reducing wasted space and associated overhead costs. Apart from inventory activities, many retailers use analytics to identify customer trends and changing preferences by combining data from different areas. By merging sales data with a variety of factors, businesses can identify emerging trends and anticipate them better. This is closely tied to marketing functions, which also benefit from analytics. Companies can harness retail analytics to improve their marketing campaigns by building an improved understanding of individual preferences and gleaning more granular insights. By blending demographic data with information such as shopping habits, preferences, and purchase history, companies can create strategies that focus on individuals and exhibit higher success rates.

Retail Analytics Examples

Retail analytics tools can support your efforts to improve operational performance and customer experience. Here are four examples of how retail businesses have successfully leveraged retail analytics to enhance decision-making and increase market share.

 

Leveraging data to optimize the customer experience:

Streamlining your data-gathering and analysis process can help measure user engagement and refine the customer experience. For instance, a company known for its fresh take on the makeup industry wanted to better interact and connect with its customers. A retail analytics solution helped them to better interact and connect with their customers.

Streamlining relationships between data and people:

To be a leading online shopping destination for style, you need to be able to understand and appropriately react to customer behavior as it happens. A retail analytics solution help build streamlined relationships between data, employees, and customers to improve customer experience and operational performance.

Gaining a 360-degree view of customer needs:

Retailers need an easily shareable and accessible single source of truth to make decisions that impact the health of their business and assess customer needs. A well-attuned retail analytics platform offers a 360-degree view of customers’ needs and data to strategize more customer-considerate products, marketing, and customer service.

Delivering self-service, real-time access to data:

As a retailer’s business grows, so does the need to scale the reporting process to understand better how to reach the right consumers at the right time. A retail analytics platform provides self-services, real-time access to shopper data and seamlessly maps internal and external data for employees, delivering a clear view of analytics for all.