Big Data in Retail: Use Cases, Challenges, and Best Practices

Retailers constantly face pressure to reduce costs by optimizing internal processes and boosting profitability through more efficient marketing. Using big data in retail can help businesses in this market tackle both of these challenges.

However, while big data analytics can provide excellent outcomes, managing it isn’t straightforward. Retailers must integrate information from multiple channels while ensuring data quality, security, and privacy.

In this article, we’ll show you how to use big data in the retail industry effectively. We’ll also highlight key use cases, address common implementation challenges, and provide practical recommendations based on Neontri’s experience in data management services.

Key takeaways:

• Big data in retail offers insights that can enhance your bottom line by helping you meet customer demands and find cost-saving opportunities.
• Applications of big data in the retail market include personalization, inventory management, predictive analytics, design and price optimization, and fraud prevention.
• Companies must solve challenges like data integration, scalability, security, compliance, and skill gaps to fully benefit from the use of big data in the retail industry.

Big data application in retail: From inventory management to personalization and beyond 

The reality is harsh but straightforward: retail businesses rely heavily on software, and failing to stay ahead of the curve can put you at risk of lagging. If a retailer doesn’t take innovation seriously, the limitations become apparent. For instance, without big data analytics, they often can’t account for complex variables, have a higher risk of human error, and lack real-time visibility.

Indeed, the impact and importance of big data in retail are hard to overestimate. Advanced analytics lets you identify patterns, forecast trends, optimize operations, and target customers better. It allows for faster decision-making on inventory, pricing, promotions, and many more business aspects.

This explains why retailers are racing to maximize the value of data combined with AI technologies, such as machine learning and natural language processing. According to Straits Research, global big data analytics in the retail market will grow from $6.25 billion in 2021 to $40.88 billion by 2030. 

To give you a clearer picture, let’s look at the prominent use cases of big data in retail with real-life examples.

Customer segmentation and targeted marketing

Retailers have access to valuable data, such as customer demographics, browsing patterns, and purchase history. Big data analytics enables them to create detailed customer segments—like frequent buyers, product enthusiasts, or holiday shoppers. 

This accurate segmentation allows for tailored campaigns that resonate with specific groups and, thus, boost sales.  Research shows that personalization improves ROI on marketing and increases sales by over 10% on average.

Examples:

• Amazon uses big data analytics for its product recommendation engine, which suggests products that customers are more likely to buy.
• Starbucks leverages customer data from its loyalty program to personalize discounts and special offers, especially to customers who haven’t visited in a while.

Inventory management and demand forecasting

​​Big data analytics allows retailers to predict demand by analyzing historical sales data, seasonal patterns, market trends, and customer profiles. Many retailers also use radio-frequency identification (RFID) technology to track products across the supply chain. When combined with advanced analytics, this data helps businesses maintain optimal inventory levels, reducing the risk of both understocking and overstocking. For instance, if a product sells faster than expected, the system can trigger automatic restocking or alert the management. The same goes for less popular products.

Examples:

• Zara gathers data from various sources, including POS systems, RFID tags, and third-party solutions, to understand customer preferences better and optimize its sales strategies.
• Walmart’s Data Café enables efficient inventory management and logistics by leveraging big data, including information from product sensors and social media.
• Office Depot uses big data in retail stores, online platforms, and warehouses to optimize the stocks of office supplies they sell.

Optimization of store placement and layouts

Big data, including sensor data, heat maps, and video surveillance, lets retailers analyze customer movement in physical stores. This way, companies can identify high-traffic areas where customers spend the most time. Similarly, the stores themselves can be strategically placed in high-traffic areas with favorable demographics.

Example:

• Best Buy, a consumer electronics retailer, uses big data to optimize layouts based on how customers move through stores, which increases domestic sales.

Efficient pricing strategies

Retail companies analyze big data to adjust pricing based on the popularity of different products and services. Adjusting pricing dynamically throughout the day or week can give a competitive edge over similar businesses. By relying on big data analytics, retailers can also identify the spending power of different demographics and tailor their pricing accordingly.

Example:

• Amazon uses AI tools that leverage big data to change its catalog pricing roughly every 10 minutes.

Data-based design modifications

Big data enables retailers to improve their product design. It works like this: the team monitors product performance and customer feedback. Based on this data, retailers can adjust product or packaging design to make it more convenient, smaller, or eye-catching.

Examples:

• Zara uses data, like sales information on every SKU and customer preferences, down to such details as cuts, colors, and buttons to create new designs, allowing it to market about 11,000 diverse items annually.
• Apple collects extensive user data to improve the design of its gadgets and high-tech devices, as well as the user interface in their apps.

Fraud detection and prevention

Big data processed by machine learning algorithms helps retailers identify malicious patterns and anomalies in transactions or customer behavior. By analyzing different data points, systems can detect unusual activity (like repeated login failures, transaction declines, phone profiles, or unusual user location) that may constitute fraud. 

Example:

• Amazon uses automated protection to detect unauthorized transactions, document forgery, complex infringements, and counterfeit products.

Properly combining AI and big data in retail allows businesses to get a lot of benefits. Big data analytics has really become a critical part of modern retail and e-commerce. However, companies must be ready to address specific challenges to apply it successfully.

Implementing big data in the retail industry: Key challenges

Technical complexity and organizational issues may hinder your organization’s ability to use big data properly and get the expected results from it. Let’s focus on the most common ones for the retail sector.

Data integration and management

Ensuring that the quality of your data is high enough for business analytics and real-time insights is very important. If the information is incomplete, unbalanced, or duplicated, the analytical systems will provide inaccurate output.

A related issue is that retailers encounter data silos if they have various isolated systems: point-of-sale devices, physical and online store transactions, mobile apps, customer service interactions, and social media. Their data formats may be inconsistent or incompatible, which may prevent you from getting a complete picture of customer interactions.

How to overcome it: 

The key to implementing big data analytics is strong data governance with clear-cut policies, standards, and processes for data management. You should define how employees collect, categorize, and access data. When selecting e-commerce platforms and services, stakeholders must ensure interoperability with existing systems.

Ongoing data quality control is also necessary to maintain accurate and usable data. Companies must regularly audit and cleanse data to prevent errors as datasets expand with new inventory items and customer segments. Implementing automated tools that validate and pre-process data as it’s collected is important, too.

Cybersecurity and compliance 

Big data involves vast amounts of personally identifiable information (PII), including customer names, addresses, and credit card data. The more data you collect, the higher the regulatory oversight and cybersecurity threats. 

Leaks can damage your reputation, drive away customers, and lead to legal penalties. According to IBM’s 2024 Report, the average data breach costs in the retail industry rose from $2.96 million in 2023 to $3.48 million in 2024. 

How to overcome it: 

Retailers need to implement advanced security-sensitive tools and policies to safeguard sensitive information. Solutions include enforcing multi-factor authentication tools, encrypting communications, restricting access to retail big data based on roles (role-based access control), and implementing AI-based threat detection tools. Some retailers may opt for more isolated on-premise deployment over cloud-based systems.

In addition to that, you should implement transparent big data collection and usage practices. Specialized de-identification (anonymization) tools can strip your datasets from PII and other sensitive data.

Scalability and technology infrastructure

Retail systems that leverage big data should be capable of handling its increasing volume, variety, and velocity. Without scalability, your performance may degrade during peak shopping times, which will cause delays in analyzing sales data, report generation, and customer service. You might also miss revenue-generation data-based decisions, like price adjustments in response to demand.

How to overcome it: 

Cloud computing offers flexibility that on-premise solutions often can’t match. These systems usually scale horizontally and vertically to handle large amounts of retail data. Beyond just scalability, cloud platforms simplify data management and analysis. Retailers can easily integrate AI-based processing systems for organizing, cleaning, and analyzing customer data.

Additionally, you may incorporate microservices architecture that breaks applications into isolated and independently deployed services. When necessary, you can scale or update individual services without affecting other critical services.

Lack of skilled professionals

Finding qualified data scientists and engineers can be another roadblock on your path to using big data. About 51% of Nash Squared’s 2023 Digital Leadership Report respondents say that big data, analytics, and data engineering are the most in-demand skills. The skills gap will make optimizing big data systems, machine learning, and predictive analytics difficult. 

How to overcome it: 

You either have to headhunt, vet, and upskill your workforce, which requires time and resources, or partner with a reliable service company that can help you fill your skill gaps in data engineering, machine learning, and AI. In some cases, these companies can offer consulting services to assist your in-house team.

Best practices for using big data in retail

Retailers must find a way to collect and process volumes of data from various point-of-sale systems and channels. Here are some strategies you can adopt.

Use data lakes and warehouses for storage

Cloud solutions like Amazon S3 are often used to store raw, unstructured data that doesn’t require computational overhead. And warehouses, such as Amazon Redshift, are optimized for structured data that is used for complex queries. Using both types of storage can help companies balance their costs with analytical needs.

Optimize data infrastructure for performance

Set up the hardware, software, and services that handle real-time analytics. Then, develop pipelines that allow the data to travel between system components and analytical tools. To avoid bottlenecks, you should regularly index key fields in databases, optimize queries, and apply caching.

Implement real-time analytics tools

Implement advanced analytics platforms that can process and analyze data as it becomes available. The right tools will assist you in tailoring promotions based on customer behavior, dynamically adjusting prices, maintaining accurate stock levels, and identifying fraudulent activity in time.

Use business intelligence tools with dashboards

Business intelligence (BI) tools transform complex data into a format understandable by non-technical staff, like charts, graphs, and maps. Examples include Tableau, Microsoft Power BI, and QlikView. Make sure your tools have customizable dashboards with key business performance indicators and the ability to filter data.

Discover the power of big data in retail with Neontri 

Big data is the reason many retailers grow and remain profitable. However, technical complexity and inefficient practices can hold you back, and you won’t get far ahead without the right practices, tools, and skills. That’s why you need a reliable technology partner.

With over a decade of experience in custom software solutions, cloud development, and data management, Neontri can help you navigate the big data landscape. Our company specializes in near real-time data analytics, empowering retailers to unlock the value of their data as it streams in.

Recently, Neontri helped a leading Polish e-commerce group develop a comprehensive data hub that connects various systems to facilitate data processing and integration. This allows for instant data transfer, selective querying, and swift access to critical information. 

The system’s efficiency in handling internal and external data sources transformed the company’s ability to make quick, data-driven business decisions. It particularly benefits the marketing team, allowing them to leverage insights generated by the platform for pricing optimization, promotions, and loyalty programs. 

That’s just one example of our portfolio of 400+ global projects. Contact us to learn more about our expertise and service offerings.

FAQs

How can small retailers benefit from big data?

Big data-driven insights help retailers optimize product offerings, pricing strategies, designs, and marketing strategies. It enables companies to collect and analyze customer behavior across multiple devices and touchpoints. This helps target the right demographics with personalized offers, leading to improved customer satisfaction, retention, brand loyalty, and revenue. Automation tools also use big data to enhance operational efficiency and cut costs.

What future trends can we expect in big data and retail?

Big data is increasingly integrating with IoT, AI, and augmented reality (AR) to transform retail. Evolving trends include smart shelves that monitor stock in real-time, more understanding AI-driven chatbots, and virtual try-ons that let shoppers experience products remotely. As technology advances, predictive analytics will become even more precise.

How does big data help retailers predict emerging trends?

By analyzing big data in retail, companies gain more valuable insights from historical sales patterns, global trends, and customer feedback. Retailers can use this information to detect shifts in consumer behavior and identify upcoming trends (like demand fluctuations due to seasonal changes or price increases due to a limited quantity of sought-after items).

What challenges do retailers face when implementing big data solutions?

Retailers struggle with data silos when information from multiple channels is not properly integrated. Scalability is another challenge because growing data volumes requires flexible system architecture, storage, and infrastructure. One more key challenge is data security and privacy laws that require companies to collect, share, and store retail data securely. Finally, the lack of skilled talent can limit the practical use of advanced analytics tools.