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In today’s highly competitive retail landscape, big data has become a game-changer. Since retailers generate vast amounts of information on an everyday basis from a variety of sources (including online transactions and social media), they can use it to obtain valuable insights into the latest trends and customer preferences. This approach fully eliminates guesswork from decision-making and reduces risks that are normally associated with retail inventory management and demand forecasting.
In this article, we talk about the use cases of big data in retail and discuss tangible, industry-specific benefits that it brings. We will also provide real-life examples from the leading companies that have already adopted big data analytics and use it to grow their business in a smart manner.
What is big data in retail industry?
To understand the significance of using big data in retail, it is first important to understand what exactly it is and how it differs from regular data.
Big data refers to enormous volumes of information, including both structured and unstructured information. Due to its size and complexity, it requires specialized and often AI-based processing tools for its analysis. Also, big data is usually characterized by the following features:
- Volume: one of the distinctive features of big data is its massive volume that is comprised of the information, collected from the most various sources.
- Variety: it includes both structured and unstructured data in different formats, including JSON files and images.
- Velocity: velocity implies the speed of data generation and processing, which often happens in real time.
- Veracity: considering the volume and variety of collected information, it is crucial to maintain its accuracy and veracity, often with the help of specialized smart tools.
- Value: the ultimate goal of big data is to bring value to users and help them make accurate, data-driven decisions.
Big data vs traditional data
For a better understanding, let’s now compare big data with traditional data using the table below:
| Big data | Traditional data | |
|---|---|---|
| Volume | Enormous, requires specialized tools for storage and processing | Manageable size, can be stored and processed in traditional databases |
| Variety | Both structured and unstructured, is comprised of various data formats | Most often structured and stored in relational databases |
| Velocity | Very high, is often processed in real time | Moderate, is often collected and processed in batches |
| Veracity | Can come from the most various sources, including less trusted ones – hence, requires more accurate verification and processing | Usually comes from trusted sources, thus being more accurate in general |
| Value | Can offer highly specialized and valuable insights and can uncover hidden patterns and trends | Is effective but limited in scope and thus suitable within specific context |
The benefits of big data analytics in retail
Now that we are clear on the definition, let’s talk about how big data is transforming retail industry. By understanding the main benefits that big data brings, you can plan the business strategy correspondingly by aligning your processes with big data analysis.
Holistic view of your customers
Customers are the driving force behind any retail business, and the key to success is a thorough understanding of your customers. For quite some time, retailers relied on guesswork and their own preferences when trying to forecast demand. But today, every business decision revolves around data, and that includes customer analysis.
Due to the variety and volume of big data, businesses can now create highly detailed customer profiles and accurately study their preferences and online behavior. This, in turn, helps retailers adjust their marketing strategies and offers and provide better personalization, thus serving the needs of all customer segments.
Better price optimization
Retail pricing is dynamic and changes in accordance with many factors, such as competition in the market, customer preferences and demand, and market trends. To effectively form pricing and ensure that it aligns with the current market situation, retailers can use big data for accurate forecasting. By analyzing past and current trends and price fluctuations, retailers can build future forecasts and use them to offer the best price at the right time.
Increased operational efficiency
Big data analytics for retail helps not only with customer analysis and price optimization but also with a number of critical processes, like inventory management and order management. By accurately predicting demand and always knowing one’s current assets, it becomes easier for retailers to avoid stockouts and overstocks. This, in turn, leads to higher efficiency in terms of managing the inventory and helps significantly cut down operating costs.
Competitive advantage
Lastly, the use of big data grants retailers a huge competitive advantage – here is how. Since the industry itself is highly competitive, it is critical to provide a high level of service paired with the ability to predict future trends and adjust your offerings accordingly. By analyzing massive data sets, retailers can successfully do so through deep understanding of what works the best and why. In addition, the collected information can be used for future marketing strategies and will allow companies to stay ahead of the competition.
Top use cases of big data analytics in retail industry
By now, you might be saying: the benefits of big data analytics in retail sector are great and all, but how exactly do I apply it to my current processes? Thus, we will now walk through the biggest use cases of big data in retail, supported by real-life examples. This should help you better understand its practical applications and possible ways how you can start using it.
Targeted marketing
Marketing drives your sales forward but unfortunately, you can’t satisfy all your customer segments with a single marketing campaign. Because most probably, these different segments will have different preferences and needs and they may even prefer different communication channels, based on their demographics.
Big data in retail industry helps successfully resolve the two issues simultaneously. First, it contributes to better customer segmentation through the analysis of massive data sets. Second, it enables you to align your marketing efforts with the newly performed segmentation and ensure that every marketing campaign precisely meets the needs of specific audience. Such an approach leads to increased revenue since tailored and personalized campaigns bring more value to customers than generic ones.
Real-life example: Office Depot
Office Depot is a huge American company that provides office supplies to customers worldwide. What keeps the company relevant and competitive is the fact that it integrates offline and online data to accurately segment its customers and allocate spendings accordingly. In this way, Office Depot always hits the spot with its marketing campaigns and makes the most of the collected big data.
Inventory management
One of the biggest challenges that many retailers face is inventory management. When retailers cannot forecast the future demand for their products, it creates the issue of inventory surplus and deficit. This, in turn, leads to annoyed customers and complete confusion with order management.
Big data helps companies analyze the past demand for specific products in specific regions, track seasonal trends and fluctuations, and build forecasts based on this analysis. In this way, companies can optimize their inventory and adjust it to future product demand, thus avoiding surplus and ensuring that they have enough products to meet future customer needs.
Real-life example: Walmart
Being one of the biggest retailers in the world, Walmart makes the most of the collected information and uses it to effectively manage its numerous stores. Through extensive analysis of the information, Walmart accurately predicts demand for specific products in different scenarios and uses future forecasts to minimize excess stock and accurately meet demand.
Fraud prevention
The issue of fraud in the retail industry remains as acute as ever. A report by Exploding Topics states that ecommerce companies may be losing approximately $48 billion to fraud on an annual basis, and the global market for ecommerce fraud detection and prevention is estimated at about $57.51 billion (360i Research). So how can big data analytics in retail industry help?
By pairing big data with ML-powered tools and real-time analysis, retailers can immediately detect anomalies in users’ behavior and identify suspicious activity. This enables them to take proactive measures and safeguard their business and customers before any significant damage is done.
Real-life example: eBay
Being an ecommerce giant, it is no surprise that eBay is on the list. The company uses big data to power its fraud detection systems and ensure seller and buyer protection. In the first case of fraud detection systems, big data analytics helps in analyzing transaction patterns and online user behavior, thus contributing to the prevention of fraudulent activity. In the second case, the smart system uses big data to identify suspicious listings or suspicious activity by sellers. This, in turn, creates a safer environment for both shoppers and sellers.
Customer feedback analysis
Customer feedback helps retail companies remain relevant by timely analyzing and understanding what customers like and dislike about particular products and services. And since big data is comprised of the most diverse information, including the one collected from social media, review pages, or even customer support chats, it becomes a valuable tool for feedback analysis.
By analyzing customer feedback, companies can effectively identify areas for improvement in their service offerings and adjust correspondingly. Needless to say, this retail big data analysis is a big competitive advantage and helps companies constantly adapt and remain relevant.
Real-life example: Zara
Just as described above, Zara uses collected data to better understand its customers through their feedback. Such constant analysis allows Zara to rapidly adjust its product design and inventory and keep up with customers’ trends and preferences.
How to implement big data in retail: tips and best practices
Let’s wrap up this article with a brief overview of the process of implementing big data analytics in retail market. Though the process will be unique for every company, the steps listed below are generic and can be considered a base for your future implementation strategy.
Define your business objectives
This step is always the first one before you implement any new technology or process. The reason for that is that many companies chase the latest tech trends without a clear vision and understanding of the value that they want to get. As a result, companies face financial losses while the processes remain the same.
Hence, before starting your big data journey, ask yourself several questions:
- What exactly do I want to optimize?
- How can big data help me achieve this goal, and are there any alternative options that I can consider?
- What are the KPIs to monitor in the future?
By setting clear goals and expectations from the start, you will be able to effectively use big data to your advantage.
Collect the information
The next obvious step is actual collection of information. You will need to use all available sources, like POS systems, social media, transactions, etc. Remember: the more information you are able to collect, the more meaningful and useful your insights will be.
When setting up your data collection process, it is important to centralize all sources into a single system. In this way, you will be able to perform a comprehensive analysis and will make use of all the collected information.
Review your data storage options
We have already mentioned that big data requires specialized tools for storage. Hence, you need to review your current storage in parallel with the collection process. This is needed to ensure that the collected information is properly stored and that your selected storage solution is scalable and powerful enough.
Process the data
Since big data comes in both structured and unstructured formats and contains various forms, it is important to clean and preprocess it before using in analysis. For big data processing, the ELT (Extract, Load, Transform) approach is used more often than the ETL (Extract, Transform, Load) one, though there are no definite rules about that.
Analyze and visualize
When your data is ready and processed, you can start analyzing it. There is a vast array of available ML tools out there that were designed specifically for big data analysis. Examples are Tableau, Apache Hadoop, and RapidMiner. When selecting a tool, pay attention to the included features and how they can help you achieve your particular business goals.
As for visualization, it is essential that your analytical tool can also present the newly found insights in a user-centric format. It usually means visualizing the data in the form of graphs, charts, and dashboards, so the insights are clear for all users and highlight the most important information in an effective way.
Monitor and adjust
Working with the data is a highly dynamic process, so you’ll need to constantly adjust your strategy based on the collected insights and changes in the market. Hence, make sure that you always receive relevant insights and that they reflect the current market situation. Based on these insights, you will be able to timely implement the needed changes and ensure your business remains relevant and competitive.
Expert Opinion
Big data analytics in the retail sector offers companies unique opportunities to improve business processes and increase profits. To achieve this, companies must initially ensure optimal data completeness while prioritizing data privacy through robust protection measures and compliance with regulations such as GDPR and CCPA.
Also, introducing analytical tools alone is not enough; it’s equally important to train staff in data handling and foster a culture of data-driven decision-making. Without this, many of the potential benefits of the implemented tools will be missed.
And finally, developing a scalable and flexible data architecture is crucial to support the growing volume and variety of data. This architecture will enable companies to quickly respond to market changes, legislative updates, and rapid technological advancements.
Final word
There are numerous use cases of big data in retail, and big data analytics is definitely something not to be overlooked. Retailers, however, should have a clear understanding of the necessary actions to take before starting to work with big data. That includes reviewing your current assets and digital infrastructure, ensuring its scalability, and hiring data scientists, if necessary. All these things need to be taken care of in advance – and SoftTeco can gladly assist you. With extensive expertise in data science and big data analysis, we offer an array of services to offer you the best possible solution.
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