Just imagine that — there are more than 26 million e-commerce websites worldwide. In 2022, at least 2.56 billion consumers made at least one online purchase. Every minute, e-commerce giants like Amazon generate $903.300 in sales, and behind each transaction lies valuable data.
But what does this mean for your e-commerce business? Invaluable insights into customer behavior, operations, competitors, and missing opportunities, which ultimately boost your bottom line.
As we live in the epoch of automation, you should consider big data analytics in e-commerce to achieve better results at a faster pace. So today, you’ll learn how it can help your online business thrive.
In the context of e-commerce, we refer to big data analytics as a sophisticated process of examining huge datasets to define hidden patterns, correlations, and insights that are otherwise impossible to detect. It usually operates in a cycle of four steps:
So, you don’t just get graphs and numbers. You obtain a roadmap to understanding customer behavior, market environment, or your operational flows.
To get a comprehensive understanding of your business landscape, you should consider fetching big data e-commerce from different sources. In fact, this could be any website, marketplace, social media, Q&A site, or any other online platform. So, let’s see what data points you can collect to gain a unique perspective on different aspects of your business.
In the Next in Personalization 2021 Report, McKinsey states that 71% of consumers expect companies to offer personalized experiences. What’s more. 76% of buyers are frustrated when shops don’t provide one. That’s why 94% of e-commerce businesses consider personalization as a key element in their success.
But every personalized experience starts with customer data collection, processing, and analysis. These steps allow you to segment your customer base into distinct categories based on buying behavior, geographic location, and even browsing history. Once you have these segmented groups, you can tailor your marketing messages, product recommendations, and even pricing strategies to suit the specific needs and preferences of each segment.
For instance, if your analytics reveal a particular segment frequently purchases eco-friendly products, you should highlight such items in their product recommendations.
34% of e-commerce businesses admit that they struggle with unavailable products in their inventory. Customers also suffer. 60% of buyers from the United States agree that out-of-stock issues negatively impacted their shopping behavior. As Deloitte suggests, one negative reputation event impacted revenue (41%) and brand value loss (41%). For example, global retail businesses lost $1.1 trillion because of poor inventory management.
All these facts hint at the need for effective demand planning. According to BigCommerce, for successful forecasting, you should use only complete data. Achieving this won’t be a problem with e-commerce data mining. Scrapers will sift through historical sales data, seasonal trends, and even external factors like economic indicators to predict future demand with remarkable accuracy.
Did you know that a 1% improvement in price optimization can lead to an 8.7% increase in profits? According to a study by McKinsey, using big data for pricing is the most effective lever to boost profitability, yet it’s often overlooked. With e commerce data analysis, you’ll dynamically adjust prices based on a multitude of factors: competitor pricing, demand fluctuations, and even customer behavior. Moreover, it’ll help you implement tiered pricing strategies for different customer segments. You may offer VIP customers exclusive discounts or bundling products to encourage higher spending.
For instance, you can monitor real-time pricing trends across various competitors. As a similar product goes on sale, you’ll get an alert, so you can either match or beat the competitor’s price.
Cybersecurity is a growing concern in the e-commerce sector. The FBI’s Internet Crime Complaint Center received 800,944 complaints in 2022. And while the total number of complaints decreased by 5% compared to the previous year, financial losses grew by 49% and exceeded $10.3 billion. So, in a landscape where a single security breach can cost millions, you should prioritize fraud detection and security.
E-commerce data analytics is not a frequent c consideration for fraud detection. However, it’s a powerful tool for helping you maintain the security of your financial operations and website in total. As you analyze vast amounts of transaction data in real-time, you can identify fraudulent transactions, unauthorized access, and other security threats. And, most importantly, take immediate corrective actions.
Big data challenges in e-commerce are not just about handling volumes of data. You may come across other hurdles, which may include:
The future of big data in e-commerce is not just promising — it’s transformative. Analytics holds the potential to revolutionize your business by helping you personalize customer experience, optimize pricing strategies, or enhance security measures.
However, as we’ve discussed, leveraging this powerful tool requires specialized skills, robust infrastructure, and a deep understanding of data science. Nannostomus will provide you with clean, high-quality data to help you make informed decisions. Contact us to learn about cooperation opportunities with our team.