How Does Business Optimize Price And Maximize Sales With Big Data In Ecommerce?

As 2025 is coming soon, staying competitive in the eCommerce industry is a must-have that requires cutting-edge technology to help businesses make smarter decisions. Then, big data in eCommerce will be the main player, aimed to optimize pricing strategies and maximize sales.

In this blog, let’s explore how big data is revolutionizing ecommerce for new growth opportunities.

What is Big data in eCommerce?

Big data refers to extremely large sets of information that are collected from various sources, such as online activities, social media, sensors, and more. 

This data is so vast and complex that traditional methods can't handle it efficiently. 

In eCommerce, big data provides so many insights into customer preferences, pricing dynamics, and competitive strategies. From then, businesses can make data-driven decisions. However, managing and analyzing such vast datasets can be challenging, requiring advanced tools and expertise.

A study by BARC highlights some of the key benefits: 69% of companies report better strategic decision-making, 54% gain improved control over operations, 52% achieve a deeper understanding of customers, and 47% enjoy cost reductions. 

For example, Amazon uses big data to analyze customer behavior and market trends, implementing dynamic pricing and personalized recommendations that account for up to 35% of its revenue. 

Use cases of big data in eCommerce industry

The benefits of big data in ecommerce for brands and retailers extend far beyond traditional analytics, it offers transformative capabilities across multiple areas. Here are some:

Analyzing customer behavior

Big data gathers information from all kinds of sources—like website visits, social media posts, purchase history, and even customer feedback. With this wealth of data, businesses can uncover patterns, like what products customers are drawn to, how they navigate a website, or what factors influence their buying decisions.

The real magic of big data is in how it processes all this information. Advanced tools can break it down to show trends and preferences, even in real-time. For example, if customers often buy two specific products together, a business can suggest those items as a bundle, making the shopping experience smoother and boosting sales.

Personalize product recommendation

Through the customer data analyzing stage, it uses AI to identify trends and predict what customers are likely to want next. For example, an online store can suggest complementary items in real time based on a shopper's current and past behavior. 

Personalize customer communication

AI-driven chatbots, powered by Big Data, help brands to handle common queries like order status or return policies instantly, reducing response times and allowing human agents to focus on complex issues. Insights from Big Data also help identify and address customer pain points, such as improving website usability.

Big data, then, can analyze customer data to deliver messages that are tailored to individual preferences, behaviors, and needs. It will create targeted email campaigns, personalized offers, and relevant product suggestions. 

For example, if a customer frequently buys skincare products, they might receive an email showcasing new arrivals or exclusive discounts in that category. Big data also boosts dynamic website content, such as personalized banners or recommendations based on real-time activity. 

This level of customization makes communication feel more engaging and relevant, higher the percentage of conversions in stores.

Besides, price optimization and conversion maximization are the top-of-mind use cases of big data in ecommerce.

How to use Big data to optimize price and increase conversions in eCommerce?

Using big data in eCommerce for price optimization and increasing conversions is like having a smart assistant that constantly works behind the scenes. Here’s to uncover:

How does it work and why is it so effective?

Big data collects and analyzes real-time information on customer behavior, competitor pricing, market demand, and inventory levels. This helps businesses implement dynamic pricing, where prices adjust automatically based on demand and competition. 

For instance, during peak holiday shopping, prices for high-demand items might increase slightly to maximize profits, while discounts on lower-demand products encourage more purchases.

What’s really exciting is how big data goes beyond just setting prices. It predicts customer responses. If a product is too expensive, data can reveal patterns like abandoned carts or low click-through rates. Businesses can then adjust pricing to find that sweet spot where customers feel they’re getting value without hesitation.

Backed by 10-years of experience in building high-converted websites, we believe the real game-changer is personalization. Big data doesn’t treat all customers the same. For instance, loyal customers might get exclusive discounts or early access to sales, while new visitors might see competitive prices to entice their first purchase. This builds trust and loyalty while maximizing conversions.

What is the step-by-step guide?

✍️ If you possess big data from online activities, congrats! You won 30% over your competitors. All you need to do next is listed below.

#Step 1: Collect Data from Multiple Sources

Gather customer, competitor, and market data from various touchpoints:

• Customer Data: Browsing history, purchase behavior, cart abandonment, and demographics.
• Competitor Data: Monitor competitor pricing, promotions, and trends.
• Market Trends: Analyze seasonal demand, economic factors, and product popularity.

#Step 2: Analyze Customer Behavior

Use big data analytics tools to identify customer patterns and preferences:

• What products are frequently purchased together?
• Which price points see the most conversions?
• How do customers respond to discounts or promotions?

#Step 3: Segment Your Audience

Segment customers into groups based on their buying habits, preferences, and purchasing power:

• High-value customers: Offer premium products or loyalty rewards.
• Budget-conscious customers: Highlight discounted or budget-friendly items.
• First-time shoppers: Provide introductory discounts to encourage conversions.

#Step 4: Implement Dynamic Pricing

Use AI and machine learning models to adjust pricing in real-time based on:

• Demand fluctuations.
• Inventory levels.
• Competitor pricing.

#Step 5: Test and Refine Pricing Strategies

Conduct A/B testing on different pricing strategies to determine what works best for your audience:

• Test various discount levels.
• Experiment with limited-time offers or free shipping thresholds.
• Analyze the impact of price changes on conversion rates.

#Step 6: Leverage Personalization

Tailor pricing and promotions to individual customers using predictive analytics:

• Recommend products based on past purchases.
• Send personalized discount codes or offers.
• Display relevant upsell opportunities

#Step 7: Monitor Results and Optimize

Track KPIs like conversion rates, average order value (AOV), and revenue. Use these insights to continually refine pricing strategies:

• Adjust discounts during off-peak seasons.
• Monitor competitor adjustments and respond dynamically.
• Evaluate how pricing changes impact customer retention.
•  during checkout.

What did Walmart do to use big data to maximize their sales?

As one of the largest retailers in the world, Walmart faced a significant challenge: managing and analyzing vast amounts of data generated from millions of daily transactions across its global network. 

With increasing competition and evolving customer expectations, Walmart needed a robust solution to enhance decision-making, optimize inventory, and deliver personalized customer experiences. This prompted Walmart to embrace big data analytics as a critical tool to stay competitive.

In 2012, Walmart leveraged big data analytics across several key areas to optimize operations and drive growth:

• Advanced Data Infrastructure: Transitioning to a 250-node Hadoop cluster, Walmart enhanced to process large volumes of data efficiently, generating real-time analysis of customer transactions and preferences.
• Social Media Insights: Monitoring around 300,000 social media mentions weekly, Walmart identifies customer sentiment and emerging trends to fine-tune its marketing strategies.
• Real-Time Decision-Making: Through its Data Café, one of the largest private cloud systems globally, Walmart analyzes multiple datasets simultaneously to address issues such as supply chain logistics and store operations in real-time.

Walmart makes a real difference to its sales

Walmart’s use of big data has led to actionable insights that directly impact sales:

• Personalized Marketing: By analyzing customer purchase history and preferences, Walmart tailors its promotional offers and product recommendations to individual customers, enhancing engagement and driving repeat purchases.
• Optimized Inventory Management: Big data helps Walmart predict product demand accurately, ensuring shelves are stocked with the right items at the right time while minimizing overstock and wastage.
• Dynamic Pricing: Walmart adjusts prices dynamically based on competitor activity and demand trends, ensuring they remain competitive while maximizing revenue.

The Results that Walmart Gained from Big Data

By integrating big data into its operations, Walmart has achieved remarkable results:

• Efficiency Gains: Walmart's Data Café enables the processing of over 40 petabytes of data daily, allowing the company to make real-time adjustments to supply chains, pricing, and inventory.
• Increased Sales: Walmart reported a 10–15% increase in online sales after implementing personalized marketing strategies and dynamic pricing powered by big data.
• Cost Savings: Walmart reduced inventory waste by leveraging big data to optimize stock levels, saving millions of dollars annually in holding costs and supply chain inefficiencies.

The future of big data in eCommerce

The future trends of big data in eCommerce is incredibly promising as it continues to shape how businesses operate and interact with customers.

AI and machine learning integration

Big data is evolving alongside AI and machine learning, making predictive analytics more accurate and actionable. Gartner predicts that by 2026, 75% of retailers will use AI-driven big data analytics to improve customer satisfaction and boost sales. 

Real-Time Personalization

The demand for hyper-personalized shopping experiences is growing. And big data will play a pivotal role in delivering them by creating dynamic content, such as personalized banners, offers, and product recommendations.

Advanced Dynamic Pricing

Dynamic pricing, powered by big data, is expected to become even more sophisticated. According to a report by McKinsey, businesses using advanced pricing strategies have seen revenue increases of 2–5% and margin improvements of 1–2%.

Blockchain and Big Data Collaboration

As blockchain technology becomes more integrated with eCommerce, it will enhance the security and transparency of big data. Businesses can ensure data authenticity while gaining customer trust, which is essential in a privacy-conscious era.

Wrapping up 

As eCommerce continues to evolve in 2025, the role of big data in eCommerce is more critical than ever. From optimizing prices and delivering personalized customer experiences to managing inventory and making data-driven decisions, big data is revolutionizing how businesses operate and compete.

Whether you’re a small retailer or a global giant, embracing big data is definitely the key to your unparalleled growth opportunities, turning complex datasets into actionable insights.  

Your business tends to harness big data to upgrade customer insights in 2025? Golden Owl is here to assist you with both eCommerce websites and AI integrations. Contact us for further consultation!