What Is Retail Analytics?

Retail analytics uses software to analyze data from diverse sales channels, revealing insights into customer behavior and trends. From identifying top-selling products to refining marketing strategies, retail analytics aids data-driven decision-making.

Read on to delve into the intricacies of retail analytics, including the job prospects of a retail data analyst.

Why use retail analytics?

Retail analytics tools capture data from your behind-the-scenes operations (including logistics, stock levels, procurement) and customer interactions (including all touchpoints and purchase trends). The merger makes for a comprehensive view of your retail landscape. Furthermore, integrating artificial intelligence (AI), machine learning, and data visualization within these tools can enable you to forecast consumer behavior, making them a superior alternative to traditional spreadsheets and text-based business intelligence reports.

Types of retail analytics

The four main retail analytics types are descriptive, diagnostic, predictive, and prescriptive. Let’s learn about each of them below:

• Descriptive analytics offers an overview of how your business performs, answering fundamental questions like “How many, when, where, and what?” 

• Diagnostic analytics helps you identify performance issues by examining data from multiple sources, such as customer feedback, financial performance, and operational metrics.

• Predictive analytics builds on insights from descriptive and diagnostic analytics to forecast trends. Predictive analytics also decodes correlations between market prices, product demand, and inventory for informed decision-making.

• Prescriptive analytics combines AI and big data to transform predictive analytics findings into actionable recommendations.

Depending on your specific business goals, you may choose to leverage one or more types of analytics. 

Business sectors engaging in retail analytics

Retail analytics can be used in a diverse range of businesses, including:

1. Brick-and-mortar stores

From identifying rush hours to tracking customer movement, conventional retail stores use analytics to improve customers’ in-store shopping experience. For instance, Amazon’s Store Analytics provides brands with data on their products, promotions, and ad campaigns in Amazon Dash Cart-enabled Amazon Go and Amazon Fresh stores in the US. The insights gathered using the analytics allow for targeted marketing, leading to better sales.

2. E-commerce platforms

Analytics within e-commerce platforms assist retailers in making informed decisions about which products to retain in their inventory and which ones to feature in upcoming flash or seasonal sales. Retail analytics can also aid in identifying unusual or suspicious transactions based on customer behavior, reducing the risk of fraud.

3. Omnichannel retailers

Omnichannel retailers are business entities that operate both offline (physical brick-and-mortar store) and online (store website). Utilizing analytics based on cross-channel sales data, omnichannel retailers can streamline customer interactions across touchpoints, boost in-store pickups, minimize inventory shortages, and proactively cater to customer demands with greater precision.

Did you know? Retail pricing laws and regulations in the United States can differ from state to state [1].

Benefits of retail analytics

Here are some perks of using retail analytics:

• Resilient supply chains: Retail analytics allows suppliers to delve into historical sales and inventory data for optimizing production and distribution processes. This reduces the likelihood of over- or under-production, minimizing unforeseen losses.

• Strategic product placement: Retail analytics tools can create heat maps, which help retailers determine the effectiveness of different product placements and store layouts. The data-driven approach allows retailers to experiment with changes and assess their impact on customer behavior and sales.

Drawbacks of retail analytics

Here are a few downsides of retail analytics:

• Data silos: As data comes from various sources and formats, integrating and harmonizing it for meaningful analysis and decision-making can be a complex task.

• Changing customer needs: Examining shoppers' past behavior through retail analytics may not provide a reliable indication of future actions. Consumer preferences, needs, and behaviors can evolve due to various factors, such as changing life circumstances, new trends, or external events.

How to choose the right retail analytics tool

Choosing the right retail analytics software requires taking into consideration the following factors:

1. Immediate business goals 

Whether variousboosting sales, enhancing customer engagement, or improving inventory management, defining your goals is the first step toward picking the right analytics tool. If your primary objective is to drive sales and revenue growth, opt for software that provides in-depth sales analytics. This includes metrics related to sales performance, conversion rates, and more. 

2. Compatibility with existing tools

As a retail business, you likely use an array of data sources and tools, including inventory management software and customer relationship management systems. Make certain that the retail software you select integrates with these systems. When matched appropriately, the combination of tools can give you a holistic view of your business operations. 

3. Cost

Beyond the initial cost, consider the total cost of ownership, which includes ongoing expenses such as licensing, maintenance, and support. Additionally, determine essential and non-essential features in the analytics tool so you only subscribe to the necessary ones. 

4. Scalability 

Non-scalable retail analytics software may require migration or costly upgrades as your business expands, potentially resulting in data disruption and increased expenses. Opting for scalable software ensures that you can adapt to the evolving needs of your business without overspending. 

Job outlook for retail data analyst

According to Glassdoor, retail data analysts earn an average annual salary of $71,675 [2]. The US Bureau of Labor Statistics (BLS) anticipates a 13 percent increase in the employment of market research analysts from 2022 to 2032 [3]. On average, approximately 94,600 job openings for market research analysts are expected each year throughout the decade.

Get started with Coursera.

Gain a deeper insight into retail marketing with the University of Pennsylvania’s Retail Marketing Strategy course, available on Coursera. Intended for beginners, you'll be able to develop value-based branding that is both profitable and customer-centric with this course. You will need approximately seven hours to complete this course, following which you will earn a sharable certificate.