Big Data E-Commerce Explained: How to Use Big Data (2024)

Have you ever had a friend who knows you so well that he can finish your sentences? And when your birthday comes around, will they give you exactly what you need or want? Based on previous conversations, decisions, your personality, and your likes and dislikes, your friend understands exactly who you are.

This is the type of relationship business owners want to build with their customers. However, knowing someone intimately takes time, and companies cannot spend years getting to know every visitor to their store or website.

But they can rely on big data. By collecting large amounts of customer data from multiple sources, companies can understand consumers in a fraction of the time. Learn about the different types of big data and how you can use it to boost your business.

What is Big Data?

Big data is what it sounds like: a lot of data. This term refers to large, complex data sets that you cannot analyze using traditional methods such as spreadsheets and graphs. Instead, big data analytics tools like Tableau and Apache Spark quickly process large amounts of mixed data.

If you’re not sure whether it’s a big data dataset or a regular dataset, use the three Vs to distinguish them:

• Volume. The amount of data is so large that it is difficult to manage manually. For example, big data can provide detailed information about individual customers rather than grouping them into general categories.
• Speed. Big data is coming at us at a rapid pace, even in real time.
• Diversity. Data collection typically involves tracking consumer behavior across different sources to better serve the target audience. For example, business owners can track direct interactions on e-commerce websites (e.g. clicks on a call-to-action (CTA) button) as well as broader market trends on social media platforms (e.g. user engagement with videos compared to other types of videos) watching content).

E-commerce companies use external data sources like Hootsuite or third-party data collected by companies that don’t directly interact with your audience. They also collect first-party data using tools like customer relationship management (CRM) software to get a comprehensive view of user behavior.

While big data can provide more information about your customers, it’s wise to be methodical. “Don’t collect information just for the sake of collecting it,” says Neil Hoyne, chief data and measurement strategist at Google, in an episode of Shopify Masters Podcast.

Instead, he suggests thinking about how you could use the data to personalize your emails or customer experiences, or to provide better value to your customers.

Structured vs. unstructured data: What’s the difference?

Datasets can include structured and unstructured data. The two work together to provide a better understanding of your audience. Here’s how they compare:

format

Structured data includes quantifiable numbers, dates, and phone numbers that you may obtain from sales forecasts, website visits, and sales transactions. Unstructured data now includes images, PDF files, audio files, videos and large text documents. For example, consumer sentiment and social media comments are not easy to quantify.

organization

Structured data is easy to organize in spreadsheets or databases, but unstructured data is either too large or not useful in columns and grids. This makes unstructured data difficult to search and retrieve because there is no predefined format.

storage

Structured data requires less storage space, while unstructured data files are typically larger and take up more storage space.

How big data is used in e-commerce

Here are some ways you can use big data analytics to achieve your business goals:

Pricing strategies

By looking at competitor pricing, market trends, and internal sales data, you can implement a competitive pricing strategy that optimizes customer behavior and profit margins. Dynamic pricing algorithms can analyze this data in real time to help you offer the best price for your customers.

Shopping experience for customers

Data collection can provide insight into how customers use your website. With so much information at your fingertips, you can offer your audience personalized content and ads to improve customer experience and increase loyalty.

Learn how they feel as they navigate your website without having to speak to them directly. For example, if you notice that visitors are leaving a page at a certain time, you can create more effective CTA buttons or redesign the page to make it more useful for your users.

Inventory management

Big data gives you a clear, real-time overview of your inventory and helps you avoid stock-outs that can lead to delays in order fulfillment and lower customer satisfaction. Big data analytics tools also use predictive analytics to predict customer demand based on seasonal demand fluctuations, sales history, and current market trends.

Fraud detection

Big data can monitor your website for unusual activity that could indicate fraud. Using big data analytics to track consumer data such as location, browsing patterns and transaction details helps identify suspicious purchasing behavior. For example, big data tools can detect whether a large number of orders use different payment methods but come from the same IP address.

Customers rely on online stores to protect their financial information. Preventing fraud can help maintain consumer trust and promote customer loyalty.

Customer service

Customer service data includes customer support emails, reviews, and chat logs. E-commerce websites can use big data tools to aggregate this diverse collection of data and identify common problems – something that would be difficult to do manually.

For example, you can compare complaint volume across different channels to identify changes or service disruptions that may have impacted the customer experience. This information can help business owners increase customer satisfaction by alleviating pain points and preventing future disruptions.

Market analysis

Big Data enables comprehensive market analysis by monitoring and processing data from a wide variety of sources. Big data allows e-commerce companies to track user sentiment on social media, analyze competitive data, and identify emerging trends.

Analyzing this data can help your team develop informed marketing strategies. For example, big data could reveal a shift in consumer behavior that you might have otherwise missed, leading you to create targeted marketing campaigns to attract those users.

Customer segmentation

Working with big data can improve your existing customer segmentation strategy. In addition to basic customer data like age, location and gender, big data can get more granular. Looking at each customer’s sales history and customer behavior patterns can reveal connections you’ve never seen before. This allows you to build specific customer segments and target them with tailored messages.

For example, a pet store could analyze past purchasing behavior and classify users into cat owners, dog owners, cat and dog owners, fish owners, and cat and fish owners. The pet store could then send a personalized email to customers who have a cat and a dog, such as a product bundle with matching Halloween costumes.

Personalized product recommendations

E-commerce websites can personalize the online shopping experience by offering users a curated selection of products. Implementing this feature requires maintaining detailed behavioral records for each website visitor. Big data tools enable personalized recommendations by tracking and analyzing data points such as online shopping behavior, purchase history and product searches, creating unique customer profiles. The information is then used to create personalized recommendations by comparing similar user profiles and identifying products that users frequently purchase together.

Offering a personalized shopping experience can help e-commerce sites increase sales and increase average order value (AOV).