Big Data: What It Is and How It Is Used in Marketing
Big Data refers to massive volumes of diverse information stored in digital form. This includes both general market statistics and personal user data, such as purchase histories and preferences.
How Big Data works and is processed
Below, we’ll take a closer look at what Big Data is and how it is analyzed.
Big Data refers to data volumes that are constantly updated and include information from a variety of sources: social media, online purchases, sensors, mobile applications, and much more. The main difference from traditional databases is that Big Data is not only larger in volume but also more diverse in structure. While traditional databases work with strictly organized tables, Big Data encompasses text, video, geolocation, event logs—essentially anything that can be useful for analysis.
Additionally, Big Data is processed faster: its analysis often occurs in real time. For instance, when you see recommendations on a marketplace or a weather forecast, it’s the result of working with Big Data.
Where data comes from
Big Data is collected from three main sources:
- Social data. This includes everything we do online: posting updates, uploading photos, sending emails. For example, millions of emails are sent globally every second. It also includes government statistics, data on movements, birth and death registrations, and health information.
- Device data. This refers to information collected by smartphones, cameras, smart speakers, sensors in cars, and smart home systems. Even street cameras and weather observation satellites contribute to this process.
- Transactional data. This encompasses everything related to purchases, money transfers, product orders, and other financial transactions.
How is all of this processed?
The volume of data is so enormous (we’re talking terabytes here) that regular software cannot handle it. Specialized technologies are used for analysis, dividing tasks into smaller parts and distributing them across multiple computers. This approach enables handling heavy workloads. The more devices involved, the faster the processing occurs.
Thanks to this, data can be analyzed in real time—for example, to tailor advertisements, forecast the weather, or monitor the performance of security systems.
Characteristics of Big Data
Big Data is typically described using five key characteristics, often referred to as the "five Vs": volume, velocity, variety, veracity, and value.
- Volume. This is the foundation of Big Data. Companies work with massive amounts of information — ranging from terabytes to petabytes.
- Velocity. Data is generated and processed at incredible speeds. Certain devices, such as smart speakers or security systems, require immediate analysis and real-time responses.
- Variety. In the world of Big Data, quantity is not the only important factor — format matters too. Data can include not only structured tables but also text, video, audio, and images. These formats are more challenging to process but offer greater opportunities for analysis.
- Veracity. Not all information is equally useful. It's critical for data to be accurate and reliable. Without this, making sound decisions becomes difficult.
- Value. Data, on its own, has no inherent value unless it's transformed into actionable insights. For example, through analysis, companies can optimize internal processes.
Data has become a true resource for businesses. For instance, major technology companies create their products and services by analyzing enormous volumes of information. A company’s success largely depends on how effectively it works with the five "Vs."
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Big Data in marketing
To develop an effective marketing strategy, brands rely on three key types of data:
Customer profile. This refers to a deep understanding of the audience. It enables companies to create personalized offers and increase customer loyalty.
Competitive landscape. Continuous monitoring of competitor actions helps identify new trends, determine unique advantages, and adjust strategies in real time.
Financial metrics. Tracking marketing investments ensures maximum returns on every dollar spent. Brands use precise analysis of campaign results to optimize budgets, choose the most effective communication channels, and improve ROI.
How do you calculate ROI? And why is it important to measure? Read the article "ROI: What It Is and Who Benefits from It."
How businesses use Big Data
Big Data solves numerous tasks in marketing:
- Audience segmentation. This helps identify different consumer groups and understand their interests. Users are segmented to offer them the most suitable products or services.
- Understanding the target audience. Analyzing collected information allows for a detailed study of customer preferences, habits, and expectations.
How do you properly segment a target audience and create a user profile? Read the article "Target Audience: Why to Define It, How to Classify and Segment It."
- Ad personalization. Data is used to optimize targeting and increase conversion rates. Companies employ many marketing tools that are often not integrated. Big Data analysis helps create a unified system for efficient marketing management.
- Behavior prediction. Big Data helps forecast demand, ad responses, and even future consumer trends.
- Personalization. Data-driven recommendations increase the likelihood of repeat purchases, stimulate cross-sales, and make discounts or promotions more relevant to each customer. For example, by analyzing massive datasets about users, Amazon developed a recommendation system that considers not only purchase history but also other factors like seasonality and upcoming events. As a result, recommendations became more accurate and relevant, significantly boosting sales.
Amazon recommendations
What are cross-sales? What cross-sell techniques exist? Read the article "Cross-Selling: Definition and Types."
- Product improvement. Feedback and analysis of product popularity help enhance product quality, enabling companies to better meet customer needs.
- Cost optimization. Data helps reduce advertising expenses, manage logistics and inventory more effectively, and allocate human resources more efficiently.
- Pricing optimization. By leveraging Big Data, companies can set more accurate prices for their products and services. Price optimization helps increase profits even with minor changes, as long as sales volumes remain stable. This highlights the critical role of proper pricing in improving business profitability.
Where Big Data is stored
User data or other information can be stored using various solutions:
- Proprietary servers — the company purchases and manages the equipment independently.
- Cloud services — data is hosted by third-party providers (e.g., Amazon), which also offer tools for data processing.
- Public access — information may be stored for free in the cloud or on private servers accessible for general use.
Each of these options has its pros and cons: proprietary servers may be cheaper but require more effort for security and technical support, while cloud solutions are more reliable but come with higher costs.
Uses of Big Data: where else is it applied
Below are examples of how Big Data is utilized across different industries.
Image processing. A few years ago, it seemed impossible for neural networks to process photos and videos on smartphones. Today, users actively apply filters and effects in mobile applications.
Recruitment. Many brands use automated systems for the initial screening of candidates. These systems quickly identify those who may not be a good fit for the position or lack interest in the job, speeding up the recruitment process.
Banking. In the banking industry, Big Data technologies protect users from fraud. Data analysis detects anomalies in customer behavior, such as unusual transactions or transfers, helping to identify and prevent fraud more effectively.
Government institutions. Government entities actively use Big Data for statistics and monitoring. This technology helps collect data on citizens, manage various processes efficiently, and ensure security.
Media. Big Data is used to assess audiences, enabling media outlets to more accurately determine viewer and reader preferences. This, in turn, influences content creation and strategy development.
Conclusion
Big Data technology is an integral part of modern marketing, providing companies with the ability to gain deeper insights into their customers, predict their behavior, and develop offerings that closely align with their needs.
By leveraging Big Data, businesses can create more precise and personalized marketing strategies. These datasets also play a key role in optimizing processes and enhancing audience engagement. This enables companies to remain competitive and respond to market changes in real time.
Year by year, the capabilities of Big Data in marketing become increasingly significant. Companies that adopt these technologies gain a clear market advantage, improving their profitability.