Paulina Twarogal
Marcin Dobosz
The retail market has changed, becoming a highly competitive place. Gone are the days when brick-and-mortar shops were the natural and first choice of customers. Today, with digital transformation in full swing, more and more customers turn to shopping online. In fact, the number of digital shoppers has grown by 40% over the last 5 years.
What does it all mean? Simply speaking, retail businesses need to put a lot more effort into staying relevant. Understanding customers well is the first step. Using smart marketing strategies is another. Only then can retailers reach specific types of customers with the right products and services.
Getting to know customers better, however, requires information on their shopping habits, purchase history, and expectations, to mention but a few. This is when Big Data analytics becomes helpful, providing retailers with invaluable insights. According to statistics, the big data market will grow by 60%, from USD 169 billion in 2018 to USD 274 billion by 2027. This shows how rapidly businesses are recognizing the potential of Big Data analytics in retail.
So, what are its benefits, real-life applications, and best implementation strategies? In this article, you’ll learn the answers.
Key takeaways:
- Big Data analytics offers invaluable insight into customer behavior, driving personalized marketing and enhancing customer satisfaction.
- With the help of Big Data analytics, retailers can optimize operations, reduce costs, and improve overall efficiency.
- Retailers who want to create tailored shopping experiences that build customer loyalty and drive long-term growth need to leverage Big Data.
What is Big Data analytics in the retail market?
Big Data analytics is now being used in almost every stage of the retail process. It opens the door to understanding patterns and getting a clear view of what customers are interested in. It also helps to work out what the popular products will be by predicting future trends.
But what exactly is Big Data? Simply put, Big Data is larger and more complex data sets from new data sources, such as customer transactions, website visits, and in-store interactions. These datasets are so large that typical data processing software can’t handle them. But these massive volumes of data, however, can be used to address business challenges that would have been impossible to solve previously.
Let’s take a look at its three fundamental characteristics to better understand how Big Data manifests itself in retail:
- Volume: It’s one of the core aspects of Big Data. Today, almost 330 million terabytes of data is created every day. The amount of data that’s generated, copied, and consumed worldwide continues to grow at an ever-increasing rate. It’s mostly due to the growth of connectivity and the development of technologies. In 2020, the volume of generated data reached 64.2 zettabytes. By 2025, it’s estimated to get as high as 180 zettabytes—that’s three times more.
- Velocity: What matters nowadays is speed. With this much generated data, businesses need to process it quickly. Real-time or near-real-time data processing plays an important role in timely decision-making and responding to changes.
- Variety: Data comes in various forms, including structured data such as databases, semi-structured data like XML files, and unstructured data including social media posts and images. Integrating these diverse types of data requires robust analytics tools that support multiple formats and provide a comprehensive view.
These features help retailers in many ways. They streamline their operations and improve customer satisfaction. By leveraging the size, speed, and diversity of Big Data, retail businesses can gain deeper insight into customer behaviors, optimize inventory management, and personalize marketing strategies.
Big Data analytics vs. traditional data analysis in retail
Are there any differences between Big Data analytics and traditional data analysis? While both share many similarities and aim to derive insights from data to support business decisions, they have a few visible differences and serve different purposes.
| Features | Big Data analytics | Traditional data analysis |
| Size | Vast volumes of data (zettabytes). | Smaller datasets (from gigabytes to terabytes). |
| Variety | Various types of data: structured, semi-structured, and unstructured data (e.g. text, videos, images). Big Data uses a dynamic schema, allowing for flexibility and the ability to store data in its raw form. | Mainly structured data that fits into relational databases or tables. Traditional databases rely on a fixed, static schema. |
| Velocity | Real-time or near-real-time processing. | Batch processing with longer intervals. |
| Data sources | Broad range of enterprise and non-enterprise-level data: social media, sensor, and audiovisual data. | Data taken from enterprise resource planning, customer relationship management (CRM), and online transactions. |
| Analysis | Takes place in real-time, providing dynamic and holistic insights as data is collected. | Occurs incrementally after events and helps understand impacts of strategies or changes on specific metrics. |
| Storage | Scalable and flexible storage solutions, often involving distributed architectures that improve scalability and performance. | Relies on traditional data warehouses and centralized databases with less flexibility. |
| Processing power | Requires high processing power to manage large, complex datasets in real-time. | Less intensive processing power for smaller data loads and periodic analysis. |
| Application scope | Used for complex, predictive, and real-time decision-making across industries like healthcare, smart cities, and manufacturing. | Routine business decisions, regular reporting, and tracking. |
Use cases of Big Data analytics in the retail industry
How is Big Data analytics used in the retail sector? There are many applications—from customer segmentation and targeted marketing to fraud detection and more. Big Data has become a powerful tool for retailers.
Customer segmentation and targeted marketing
Figuring out which customers are likely to want a particular product and how best to present it to them is the key here. Big Data analytics gives businesses a better understanding of their customers. They can find out more about their customer preferences, purchasing habits, demographics, and other important types of data. Equipped with this knowledge, retailers can then create customer profiles which help tailor products, services, and marketing strategies to the needs of specific customer segments. As a result, advertising is more effective.
Amazon is well-known for its successful use of customer segmentation. For years, the world’s largest online retailer has been creating personalized shopping experiences and offering customized products. How? The corporation has been collecting massive amounts of data from its customers, amounting to around one exabyte of their purchase history.
By using this data coupled with customer browsing behavior, Amazon can easily identify groups of customers who prefer a certain type of product. For example, 30% of their customers are interested in electronics. This information helps Amazon develop targeted marketing campaigns to promote new electronics releases or related accessories to this group.
Inventory management and demand forecasting
Almost 31% of online shoppers are likely to switch to a competitor if the product they’re looking for is unavailable on their preferred site. This number may double if this situation happens again. Failing to have a stock that meets the demands of customers might put business at risk of missing out on sales opportunities and even losing customers in the long run. Big Data analytics gives retailers a chance to peek into past sales, seasonal patterns, and market conditions to forecast future sales more accurately. This leads to better inventory management and cost reductions.
Walmart might be a good example of a retailer that uses advanced analytics to analyze historical sales data across its stores. Advanced algorithms can accurately predict demand for different products in different areas, improving inventory management. This way, Walmart minimizes excess stock levels and provides its customers with the products they look for. Therefore, with data-driven forecasting, Walmart has reduced excess inventory by 30% and saved USD 2 billion annually.
Fraud detection and prevention
The threat of fraudulent activities in the retail industry is growing. It’s projected that global merchant losses from online payment fraud would top USD 343 billion between 2023 and 2027. To analyze massive amounts of data, Big Data employs modern analytical tools and machine learning algorithms. This helps detect patterns and anomalies that might be hard to spot otherwise. Moreover, Big Data can learn from new information and adapt over time.
Walmart uses big data analytics to detect and prevent fraud. Because of processing large amounts of transaction data Walmart’s systems can identify unusual patterns and flag potentially fraudulent activity.
Pricing strategy optimization
Setting the right price for products is important. When done wrong, it can lead to lost sales and profits. With Big Data at hand, retailers can figure out the best prices by looking at market conditions, their competitors prices, and customer behavior.
Target, the largest discount retailer in the United States, leverages Big Data analytics to dynamically optimize pricing for various products. By deeply analyzing historical data, Target gains insight into which products are popular with specific customer groups. It can then send targeted coupons and offers to these groups. This helps them remain competitive and attractive.
Customer sentiment analysis and feedback
Customer sentiment is a metric used to measure how customers feel about a given brand. By examining vast amounts of customer feedback from platforms such as social media, reviews, and support interactions, Big Data analytics can determine customer feelings and pinpoint areas that need improvement.
Starbucks, for example, tracks how people feel about their brand by examining social media posts and reviews. Thanks to this they can quickly respond to negative comments and build on positive feedback to improve their brand image and keep customers satisfied.
Benefits: Why is Big Data analytics important for retail?
Using Big Data effectively can benefit businesses, large and small, across different industries. Retail is no exception here. You’ve already had a chance to see how retailers leverage Big Data analytics, so let’s take a closer look at the overall advantages Big Data brings to the table.
Enhanced customer satisfaction
The ability to track consumer data gives businesses a better understanding of their customer interactions and needs. With this information at hand, management can make smarter decisions when it comes to marketing campaigns and the range of products in their offer. Moreover, by analyzing customer feedback from various channels, retailers can address concerns and improve their overall service quality.
Offering personalized retail experiences for customers shopping both in brick-and-mortar and online stores contributes to their satisfaction. This, in turn, increases the chances that they’ll purchase again from the same brand and become loyal clients.
Operational efficiency and cost reduction
Another benefit of using Big Data analytics in retail is the improvement of supply chain management. This naturally leads to streamlined operations and reduced operational costs. By analyzing data throughout the supply chain, retailers can optimize logistics, predict demand, and manage inventory much more efficiently.
Furthermore, better supply chain visibility can help identify potential disruptions early. In this case, it’s way easier to solve problems proactively and maintain smooth operations.
Innovation and adaptability to market changes
The retail market is constantly evolving, with new consumer preferences and trends emerging every so often. To have a competitive edge, retail companies need to align their strategies, products, and services with the changing tastes in the industry. Doing so might not be easy. However, with Big Data analytics, things look more promising. Big Data helps to innovate—introduce new products or adjust existing ones to meet shifting needs.
Increased revenue
Optimizing pricing strategies, inventory management, and marketing activities based on data-driven insights can considerably boost revenue. How? Well, Big Data analytics opens the door to identifying high-value customer segments. This allows for targeted promotions and personalized offers that drive sales. Moreover, retailers can predict demand more accurately than before. So, that means no stockouts and lost sales.
Best practices for implementing Big Data analytics
Even though the steps to implement Big Data analytics may vary, depending on your business goals and data processing specifics, there are a few you might follow.
Define clear business objectives
Start with clearly outlining your business goals that Big Data analytics will address. What specific problems would you like to solve? Is it boosting customer satisfaction, optimizing inventory, or maybe enhancing marketing efforts? Whatever the reason, well-defined goals are the very first step to the effective implementation process. Also, check if the Big Data solution is feasible—estimate the cost and ROI of the project and evaluate operating expenses.
Assess current data infrastructure
The second step is to carefully examine your current data infrastructure. Determine its strengths and weaknesses as well as define the formats, sources, and quality of the data. It’s important to know precisely the tools and procedures you use to manage data at the moment. As a result, you’ll find it easier to identify the data infrastructure investments your business might need to support Big Data efforts.
Choose a data platform
Selecting the right data platform is yet another step. This involves choosing the infrastructure and software for storing, processing, and analyzing your data. Companies can choose between on-premise, cloud, or hybrid platforms, based on their budget, security, and scalability requirements. Also, consider the platform’s compatibility, performance, and features like data integration, quality, governance, and data visualization.
Design your data architecture
Next, plan the overall structure and organization of your data systems. This means creating a clear plan for how your data will be organized and managed. To do so, you must decide on the different components you need, like data lakes, data warehouses, and ETL processes, and how they will work together.
At this point, it’s important to make sure the design supports data governance, security, and compliance. What’s more, the architecture should be flexible and scalable. After all, as your business grows, the number of big data sources and data volume will grow too. So, a scalable architecture will help you adapt to the changing needs of your business.
Build and integrate your data infrastructure
Once the platform is chosen and the architecture is designed, it’s time to implement them into practice. Start by setting up the data platform and configuring the necessary hardware and software. Then, develop data pipelines to connect and integrate various data sources.
Implement storage solutions, such as databases and data lakes, to ensure that data flows smoothly between components. Finally, test, validate, and monitor your setup to ensure it operates effectively and meets your data needs.
Create and communicate insights
After integrating your data infrastructure, it’s important to share your data insights by presenting and interpreting your analysis results. You can do it by using dashboards, reports, charts, and graphs for visualization. However, keep in mind you should tailor the insights to your audience (executives, managers, or customers) and focus on the key findings, trends, and patterns that are most relevant for them.
Tools for Big Data analytics
The choice of Big Data analytics tools has a significant influence on the success of your Big Data initiative. That’s why before making a decision, consider factors such as scalability, performance, cost, and compatibility with your organization’s technological landscape. It’s also wise to look for tools with AI features, as they can greatly simplify the data analysis process.
Microsoft Power BI
Power BI stands out as a powerful business intelligence platform. Its features like integration, cleansing, and security simplify data management. Power BI transforms raw data into actionable insights and ensures secure storage and easy accessibility. Moreover, in case when data is accidentally lost, the Query Editor can effortlessly restore it to its original state.
Key features:
- Interactive data visualization: Power BI allows for creating engaging dashboards and reports to reveal market trends and insights.
- Seamless integration with Microsoft Excel, PowerPoint, and Teams.
- Easy data management: Users can easily import from Excel and export visualizations to PowerPoint. This provides them with a unified data experience.
Qlik Sense
Designed to help users explore and visualize their data with ease, Qlik Sense is fast and responsive, handling large and complex datasets. It provides valuable insights, which are displayed through charts, graphs, and tables. The platform also includes AI-driven tools for data preparation, machine learning, and predictive analytics that help forecast trends and support smart decision-making.
Key features:
- User-friendly interface suitable for all skill levels and scalable architecture for a variety of analytical needs.
- Unique data discovery and global search.
- In-memory storage technology support.
Tableau
This tool offers a strong focus on data visualization. It has intuitive drag-and-drop features and a wide range of chart types. So, whether you want to explore sales trends, customer behavior, or financial performance, Tableau offers a wide range of visualization options to suit your needs.
Key feature:
- Ability to handle large data extracts for offline analysis.
- Scalability: Tableau can be deployed on-premises or in the cloud, offering flexibility for different business needs.
Looker
Looker is a powerful business intelligence and data analytics platform that excels in embedded analytics and data exploration. Unlike traditional BI tools, Looker focuses on delivering data insights directly within business user workflows. It might be invaluable for creating custom dashboards for sales teams, inventory management, customer analytics, and more.
Key features:
- Strong focus on user experience and accessibility.
- Comprehensive data governance features to protect sensitive information.
- Scalability to handle growing data volumes and user demands.
Neontri’s case study
Neontri helped one of the biggest e-commerce companies in Poland to comply with EU Omnibus Directive by building a powerful data hub using Big Data. This hub made sure all their systems worked together smoothly and kept their data up-to-date and in line with the regulations.
The platform was designed to quickly store and process large amounts of data, which was especially useful for the marketing team. They could set optimal product prices based on real-time data. By using Big Data they reduced storage costs and made better decisions.
Conclusion
Big Data analytics has become an indispensable tool for retailers who want to succeed in today’s competitive market, giving them exciting opportunities for innovation and competitive advantage. By using Big Data, companies of different sizes can get a better understanding of their customers. As a result, they can optimize their operations and drive sales.
Not sure where to start? Reach out to us—at Neontri, we can help you with implementing robust data architectures and integrating advanced analytics platforms to gain valuable customer insights. Our data management solutions will help you manage inventory efficiently, personalize customer experiences, and customize marketing strategies for the best results.
FAQ
How can small retailers use Big Data analytics?
Even small retailers can benefit from Big Data analytics. They can leverage it to:
- Understand their customers better. Big Data analytics allows them to analyze customer purchasing patterns, social media interactions, and loyalty program data. As a result, retailers can learn more about their customer preferences and tailor products and services.
- Optimize inventory through using sales data to predict demand, reduce stockouts, and avoid overstocking.
- Enhance pricing by analyzing competitor pricing and customer demand to maximize profitability.
- Improve customer service by identifying pain points and personalizing support.
- Effectively target marketing with data-driven campaigns and increased engagement.
What are examples of successful implementation of Big Data analytics in retail?
Amazon might be a good example of how Big Data can transform a business. By analyzing vast amounts of customer data, they’ve learnt how to personalize their offer and recommend products people are likely to love. This, combined with efficient inventory management based on data-driven demand forecasts, has fueled their growth.
Walmart is yet another example of a successful Big Data implementation. They’ve used it to optimize its supply chain. By analyzing sales data and external factors, they’ve streamlined logistics and reduced costs. Additionally, Walmart has used data to refine store layouts. This improved customer experience and increased sales.
How important is data integration for effective Big Data analytics in the retail industry?
Data integration is crucial for effective Big Data analytics in retail. It involves combining data from various sources such as sales, inventory, customer data, and external data into a unified view. This integrated data gives retailers a better understanding of the business and enables accurate analysis and informed decision-making. Without proper integration, data’s value is limited, and insights may be inaccurate or incomplete.
