Big-Data Ecommerce Explained: How to Use Big Data

Have you ever had a friend who knows you so well they can finish your sentences? And when your birthday rolls around, they gift you exactly what you need or want? Based on past conversations, decisions, your personality, and likes and dislikes, your friend clearly understands who you are.

This is the type of relationship business owners strive to develop with customers. However, it takes time to know someone on a deep level, and businesses can’t spend years acquainting themselves with each visitor to their store or website.

But they can turn to big data. By collecting large amounts of customer data from different sources, businesses can understand consumers in a fraction of the time. Learn more about different types of big data and how it can be used to boost your business.

What is big data?

Big data is what it sounds like: lots of data. This term refers to large, complex datasets you can’t analyze with traditional methods like spreadsheets and graphs. Instead, big data analytics tools, like Tableau and Apache Spark, quickly process high volumes of mixed data.

If you’re unsure whether you’re dealing with a big data or regular data set, use the three Vs to help you distinguish them:

• Volume. The data is so large it’s difficult to manage manually. For example, big data can provide granular information on individual customers instead of grouping them into general categories. 
• Velocity. Big data comes in at a fast pace, including in real time. 
• Variety. Data collection typically involves tracking consumer behavior across various sources to serve the audience better. For example, business owners can observe direct interactions on ecommerce websites (i.e., clicks on a call-to-action (CTA) button) as well as broader market trends on social media platforms (i.e., users’ engagement with videos over other types of content). 

Ecommerce businesses use external data sources such as Hootsuite, or third-party data collected by entities that don’t directly interact with your audience. They also collect first-party data through tools like customer relationship management (CRM) software to get a comprehensive view of user behavior. 

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

Instead, he suggests thinking about how you might use the data to personalize your emails or customer experiences, or deliver 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
• Organization
• Storage

Format

Structured data include quantifiable numbers, dates, and phone numbers that you might get from revenue projections, site visits, and sales transactions. Meanwhile, unstructured data include images, PDF files, audio, videos, and large text documents. For example, consumer sentiments 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 more difficult to search and retrieve because there isn’t a predefined format.

Storage

Structured data uses less space, whereas unstructured data files are typically bigger and take up more storage space.

How big data is used in ecommerce

• Pricing strategies
• Customer shopping experience
• Inventory management
• Fraud detection
• Customer service
• Market analysis
• Customer segmentation
• Personalized product recommendations

Here are a few ways you can use big data analytics to reach your business goals: 

Pricing strategies 

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

Customer shopping experience

Data collection can reveal how customers use your website. With so much information at your fingertips, you can offer your audience personalized content and ads to help improve the customer experience and build loyalty. 

Learn how they feel about navigating your website without needing to speak to them directly. For example, if you see a pattern of visitors exiting a page at a certain point, you might create more effective CTA buttons or redesign the page to make it more useful to your users.

Inventory management

Big data gives you a clear, real-time view of your inventory to help you avoid stockouts, which can lead to fulfillment delays and lower customer satisfaction levels. Big data analytics tools also use predictive analytics to forecast customer demand through seasonal demand fluctuations, sales history, and current market trends.

Fraud detection

Big data can monitor your website for unusual activity that may indicate fraud. Using big data analysis to track consumer data like location, browsing patterns, and transaction details helps identify suspicious purchasing behaviors. For example, big data tools can flag if 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 support customer retention.

Customer service 

Customer service data includes emails, reviews, and customer support chat logs. Ecommerce websites can leverage big data tools to synthesize this diverse collection of data and identify common issues—something that would be challenging to do manually. 

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

Market analysis 

Big data enables comprehensive market analysis by monitoring and processing data from a wide range of sources. With big data, ecommerce companies can track user sentiment on social media, analyze competitor data, and spot emerging trends. 

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

Customer segmentation

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

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

Personalized product recommendations

Ecommerce websites can personalize the online shopping experience by providing users with curated product selections. Implementing this feature requires maintaining detailed behavior records for each site visitor. Big data tools enable personalized recommendations by tracking and analyzing data points like online shopping behavior, purchase history, and product searches and creating unique customer profiles. Then the information is used to create personalized recommendations by comparing similar user profiles and identifying products users frequently purchase together. 

Offering a personalized shopping experience can help ecommerce websites drive sales and increase average order value (AOV).