Retail analytics is how retail global leaders like Walmart, Costco, or Schwarz Group (Lidl & Kaufland) use data and advanced technologies to understand our customers better and optimize our operations. In a world where e-commerce is rapidly expanding, it’s easy to think the future of retail is solely online. But despite the growth of online shopping, over 80% of global retail sales still happen in physical stores. In 2022, the global retail market generated over $27 trillion in sales, and by 2030, this figure is projected to exceed $38 trillion, fueled by both traditional and digital channels. All Leading global physical retailers including Tesco, Costco, and Carrefour continue to thrive by combining the strengths of brick-and-mortar stores with digital innovations. These companies understand that while e-commerce is growing, the physical retail experience remains crucial for engaging customers, building trust, and maintaining strong sales worldwide.

The Rise of Omnichannel Retailing
The future of retail is omnichannel—a seamless blend of online and offline shopping. Customers today move fluidly between digital and physical experiences, researching products online, seeing targeted ads across multiple platforms, and then visiting physical stores to complete their purchases. This trend is global, with major retail markets like the U.S., Europe, and growing economies such as China, India, and Brazil leading the charge. By 2030, emerging markets will account for a significant portion of this growth, driven by rising consumer spending, expanding middle classes, and improved digital infrastructure. The below stats will give you a picture of how big it is.
By 2030, China’s middle class will exceed 1 billion people, fueling a surge in demand for premium products and imported goods. This economic shift will help propel China’s retail market to more than $8 trillion, E-commerce sales are forecast to surpass $4.5 trillion, solidifying its status as the world’s largest and most influential retail economy.
India’s retail market is projected to soar to $2 trillion by 2030, driven by a sharp rise in consumer spending, rapid urbanization, and the expansion of its middle class. By then, India’s middle class will grow to over 400 million people, while nearly 40% of the population will live in urban areas. This urban shift will unlock new retail opportunities, as increasing disposable incomes and a demand for modern, convenient shopping experiences reshape the retail landscape across the country.
What is Retail Analytics?
Retail analytics is all about collecting, processing, and understanding the data that flows through retail stores, websites, apps, or various other customer touchpoints to achieve customer 360. It helps understand customer behavior, optimize inventory, manage the supply chain, forecast demand, and boost company performance, profitability, and overall customer experience.
In simple words the leader of a large retail chain, like Walmart, will see retail analytics as the tool that helps to understand their customers and business better by using data. It’s like having a map that shows us what’s happening in our stores, online, and across the supply chain. For example, retail analytics helps us figure out:
- What customers want: We can see their buying habits and preferences, which allows us to offer the right products at the right time.
- How to manage inventory: It tells us when to stock up or reduce inventory, so we never have too much or too little of a product.
- What promotions work best: We can analyze which discounts or campaigns drive sales and adjust our marketing to be more effective.
In simple terms, retail analytics turns data into actionable insights, helping us run the business more efficiently and serve our customers better.
At its very core, retail analytics strives to achieve three primary goals: understanding customer behavior, identifying patterns, and driving operational optimization to enhance overall business performance. It is also keeping track of customer interactions at all times and at all levels. It is incredibly complicated but the use of new technology and efficient data management is the key.
Companies worldwide are relying on Retail analytics to take a variety of important decisions like opening a store in a particular city or particular street which is next to the biggest competition. It also could be decisions on what products on what shelves makes sales go higher, The products next to check-out counters which helps in impulse buying, The number of checkout counters, discounts, and loyalty points for customers.
The list is endless, almost every decision in a retail store can be linked to Data and thus to retail analytics. This is not only store analytics we are talking about adding shopping apps, loyalty engines, websites, messaging apps, and other outreach channels like email.
Achieving Customer 360 is what customer wants but it is incredibly difficult to implement. Customer wants if you had one interaction with the company, the company should use the points from that interaction and suggest you next steps for smooth onboarding. Now imagine there are 100s of touch points, so many human interactions involved the company is most likely to lose track of what is promised and what is not. In spite of this difficulty, it is imperative to track data and give the customer what they need. There is no respite, no escaping from Retail Analytics or Efficient Retail Data management.
Check Retail Analytics Driven by SCIKIQ
As a Vice President at a retail giant like Walmart, you witness firsthand the incredible power of retail analytics. Walmart processes an astonishing 2.5 petabytes of data every hour, capturing nearly every customer interaction, from browsing habits to purchase decisions. E-commerce platform for everyone, Shopify has facilitated over 1 billion orders, generating more than $200 billion in sales. None of this would be possible without a sophisticated retail analytics and data management system to harness this information.
It’s like having a treasure trove of insights at your fingertips, allowing you to make data-driven decisions that boost profitability. Imagine it as a crystal ball that tells you exactly what your customers want and how to deliver it—whether it’s optimizing inventory, refining promotions, or personalizing the shopping experience.
In today’s fast-paced retail landscape, understanding performance metrics isn’t just a bonus—it’s essential. Measuring Key Performance Indicators (KPIs) like sales trends, customer lifetime value, and inventory turnover helps steer the business toward success, ensuring that every strategic move is based on real-time, actionable data. Retail analytics is the backbone of modern retail, providing the clarity needed to stay ahead in a competitive market.
Check the top 10 KPIs to track for Retail companies and 50 KPIs for an e-commerce company.

Evolution of Retail Analytics
Retail analytics has undergone a significant evolution, progressing from manual inventories and basic sales records to sophisticated systems driven by technological advancements. With the introduction of electronic cash registers and point-of-sale (POS) systems in the 1980s, retailers gained more accurate sales data and streamlined transaction processes.
In the 1990s, the concept of data warehousing emerged, enabling the consolidation of data from multiple sources into a centralized repository. This facilitated a more comprehensive analysis of retail data, leading to the development of business intelligence (BI) tools.
The proliferation of digital technologies and the exponential growth of data in the 21st century gave rise to the era of big data in retail analytics. Artificial Intelligence (AI) and Machine Learning (ML) are now employed to churn data at massive levels but there was one more change coming and no one predicted it before it actually happened. It is the advent of omnichannel retailing, where customers interact with brands through multiple channels and it has changed Retail significantly.
Retailers now have to track customer journeys across online and offline touchpoints, analyze customer behavior, and provide personalized experiences, making retail analytics more challenging than ever before. Customer-centric analytics allows businesses to understand preferences, anticipate needs, and deliver tailored offerings and this is giving rise to Customer 360 which we will discuss later.
Components of Retail Analytics, various KPI and Metrics
Retail analytics is like solving a puzzle using information from a store. We collect data about sales, customers, and other things. Then we look at the data to find patterns and understand how things are going. This helps us make smart decisions about prices, what to sell, and how to make customers happy. This also helps us to achieve what we call Customer 360. There are four main steps to retail analytics before you even begin with Data Analytics.
- Data: We gather information from cash registers, customer programs, websites, and social media or almost all touchpoints for the customer and start managing the data.
- Analysis: We study the data to find patterns and learn important things about our customers and how the store is doing or the website or the email campaign you just sent.
- Insights: The things we learn from analyzing the data help us make good choices about pricing, products, and other important parts of the store or the website or app. This is when you know how customers is behaving, what products are selling, and why.
- Action: Finally, we take action based on what we learned. This might mean changing prices, improving products, or making the store or online experience better for customers.
There are different types of retail analytics, this is usually the same statistical techniques that we apply to any data situation.
- Descriptive analytics: What’s happening
- Diagnostic analytics: Why it is happening
- Predictive analytics: What will happen
- Prescriptive analytics: What to do
In retail, data analytics plays a pivotal role in transforming how businesses operate, make decisions, and interact with customers. By applying retail analytics, retailers can extract valuable insights that lead to smarter decisions, increased profitability, and enhanced customer experiences. Retail analytics helps us leverage data across multiple areas of the business to optimize operations and make data-driven decisions.
Here are key areas where retail analytics can be applied, along with important KPIs to measure for each:
1. Sales Analysis
- Application: Identify top-selling products, track sales performance across different periods, and forecast future demand to ensure stock availability and capitalize on high-demand products.
- KPIs to Measure: Sales per store, revenue per product category, gross profit margin, sales growth rate, and average transaction value.
2. Customer Segmentation
- Application: Group customers based on demographics, purchase behavior, and preferences to create targeted marketing campaigns, personalize offers, and improve customer retention.
- KPIs to Measure: Customer lifetime value (CLV), customer acquisition cost (CAC), repeat purchase rate, and customer churn rate.
3. Inventory Management
- Application: Optimize inventory levels to ensure the right products are available at the right time, reduce stockouts, and minimize excess inventory.
- KPIs to Measure: Inventory turnover, stock-out rate, days of inventory on hand (DOH), and carrying cost of inventory.
4. Pricing Optimization
- Application: Analyze pricing strategies by evaluating market conditions, demand, and competitor pricing to determine the optimal price points that maximize profitability while staying competitive.
- KPIs to Measure: Price elasticity, average selling price (ASP), markdown percentage, and gross margin return on investment (GMROI).
5. Store Performance Analysis
- Application: Evaluate the performance of individual stores or retail locations to identify strengths and weaknesses, allocate resources efficiently, and improve operational efficiency.
- KPIs to Measure: Sales per square foot, foot traffic, conversion rate, basket size, and store profit margin.
6. Promotional Effectiveness
- Application: Measure the impact of marketing campaigns and promotions to determine their effectiveness, return on investment (ROI), and how they influence customer behavior.
- KPIs to Measure: ROI on marketing spend, promotional lift, cost per conversion, and incremental sales.
7. Market Basket Analysis
- Application: Understand customer purchasing patterns by analyzing product combinations that are frequently bought together to uncover cross-selling and bundling opportunities.
- KPIs to Measure: Average order value (AOV), cross-sell ratio, and product affinity score.
8. Supply Chain Optimization
- Application: Streamline supply chain operations to reduce costs, improve delivery times, and enhance overall efficiency across sourcing, manufacturing, and logistics.
- KPIs to Measure: Order fulfillment rate, lead time, supply chain cost per unit, and return rate.
9. Marketing Analytics
- Application: Measure the effectiveness of online customer behavior, social media campaigns, and advertising performance, to enhance marketing ROI and optimize spending.
- KPIs to Measure: Return on ad spend (ROAS), cost per acquisition (CPA), customer engagement rate, and click-through rate (CTR).
The Top 10 Retail KPIs you can track with the help of retail analytics. Also The top 10 e-commerce KPIs. to track
Achieving customer 360 is the aim of Retail Analytics
In the modern world of Omnichannel Retail and e-commerce, The larger aim for retail analytics, with all tools and techniques, and data management is to achieve a Holistic view of customer activities which is called Customer 360.
Customer 360, also known as a 360-degree view of the customer, refers to a comprehensive and holistic understanding of a customer’s interactions, preferences, interactions, and behaviors across multiple touchpoints and channels.
It involves aggregating and integrating data from various sources to create a unified profile of each customer. A Customer 360 view takes into account both online and offline interactions, such as purchases, website visits, customer service interactions, social media engagements, messaging, responses to emails, App activity, and more.
The Purpose of Retail Analytics is to achieve Customer 360 and a higher Customer Lifetime Value (CLV). Customer lifetime value (CLV) is a metric that measures the total net profit a company can expect to generate from a customer throughout their entire relationship with the company. A Good example is Apple: Apple products, such as iPhones, iPads, and MacBooks, tend to have a high CLV because they are often replaced every few years. The average annual CLV for an Apple customer is estimated to be $2,000. Adobe Creative Cloud: Adobe Creative Cloud is a software subscription service that offers a suite of creative tools for designers, photographers, and other creative professionals. The average annual CLV for a Creative Cloud subscriber is estimated to be $1,200.
In today’s omnichannel world, companies must prioritize retail analytics to achieve customer 360. By leveraging data and advanced analytics, businesses can gain insights into customer interactions, preferences, and behaviors across various channels. This enables personalized experiences, optimized operations, and long-term customer loyalty. Retail analytics is the key to success in delivering seamless and tailored customer experiences in the modern retail landscape.
At the same time, Effective data management is crucial for successful retail analytics. It involves collecting relevant data from various sources and integrating it into a unified view for Customer 360. One cannot achieve excellence in Retail analytics unless supported by a world-class Data management platform. SCIKIQ Offers Data management solutions for Retail Analytics and achieving customer 360. Data Analytics companies like DAAS LABS have their own Centres of excellence for Retail. By mastering data management, businesses can unlock the power of retail analytics to gain actionable insights, improve customer experiences, optimize operations, Supply chains, and drive growth and profitability in the dynamic retail landscape.
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