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What is eCommerce analytics?
Ecommerce analytics refers to the application of a tool or strategy to analyze data to produce useful insights. Initial to any stage of planning is data analytics. According to MicroStrategy’s study, 52% of enterprises leverage advanced analytics data to provide greater insights. It also helps them implement better key strategic initiatives and improve relationships with customers and business partners.
The stage of analytics can make or break a business’s performance, and it’s important whether you’re running a brick-and-mortar or online business. The differences lie in the indicators and analytics tools.
See more:
- Mobile Commerce Metrics to Track and Improve 2025
- Essential eCommerce KPIs for Online Business in 2025
- 9 Tips to Increase eCommerce Conversion Rate Quickly
eCommerce analytics examples
Examples of leveraging e-commerce analytics by brands include:
1. Amazon: Dynamic Pricing and Personalization
With Amazon, such analysis sets or suggests product prices dynamically depending on how competitors vary their prices or how users configure their buying habits. The outcome maximizes sales while keeping the business competitive. Moreover, Amazon applies advanced analytics to segment its customers for personalized recommendations so that consumers may buy with a much better chance of repeat.
2. Walmart: Inventory Management and Pricing Optimization
Among other applications, e-analytics at Wal-mart helps optimizes inventory levels through rapid dynamic pricing. By checkmating sales data against indices from the other end of the market trend analysis, Walmart profits from testing its assignment on changing prices in real-time to ensure “spend” adequacy on customer demand while improving bottom-line efficiency and profitability.
3. Netflix: Value Analysis for Customer Lifetime
Netflix predicts the Customer Lifetime Value of its subscribers to calculate intelligent content recommendations for maximizing experience and retention and keeping customers so happy and loyal.
Types of eCommerce analytics
There are many kinds of eCommerce analytics for different purposes to understand and improve online activities.
Some types of e-commerce analytics are:
- Descriptive analytics: summarizes historical data rather than showing current sales, revenue, traffic, conversion rates, or demography of customers.
- Predictive analysis: predicts future trends and outcomes using historical data and statistical algorithms. For instance, it can predict customer lifetime value, product demand, market trends, etc. Such information would help a company retrodict customer behavior and make proactive decision-making.
- Prescriptive analytics: provide recommendations for actions to be taken beyond predictions thus aiming at desired goals in the first place. It identifies the best strategies for maximizing revenues while minimizing costs and improving operations.
- Churn analysis: focuses on detecting customers who are no longer engaging with the company or purchasing. The pattern and reasons for churn can be studied to find strategies that a company could employ for targeted retention.
- Sentiment analysis: analyze the text data of customer reviews, mentioned in social media, or any other source, to find the sentiment strains underlining that particular business. It helps in measuring customer satisfaction and understanding the points of improvement needed in it.
eCommerce analytics across customer lifecycle
eCommerce Analytics is fundamental in establishing and improving the customer journey. Organizations will track performance, improve decision-making, and enhance experiences with customers by concentrating on five distinct sections in the customer lifecycle. As you can see, these five stages break down into metrics that help drive the organization to be successful.
1. Discovery: How Customers Discover Your Business
This is the option depicting how a possible customer discovers your business and, thus, how they guarantee the next engagement with it.
- Impressions: How many times will an ad, a product listing, or a webpage be displayed to users, no matter whether they interact with it or not.
- Reach: The total count of unique users exposed to your content over a specific timeframe.
- Engagement: Measures user interaction, e.g. likes, comments, shares, clicks, or time a user spent on a page.
2. Customer Acquisition: Converting Visitors into Customers
This involves the conversion of visitors into customers, charging the cost of a customer.
- Cost Per Acquisition (CPA): Cost incurred by acquiring a customer, calculated by total cost of campaign divided by the total number of customers acquired.
- Click-Through Rate (CTR): Percentage of users who click on a link after viewing an advertisement calculated by clicks divided by impressions multiplied by 100.
- Cost Per Lead (CPL): The cost of acquiring a potential customer acquired by dividing the campaign cost by the total number of leads it generated.
3. Conversion: Converting Online Users into Purchasers
Encourage customers to complete certain actions for which they have visited the site, such as making purchases.
- Average Order Value (AOV): The average amount spent per transaction, calculated by dividing total revenue by the number of orders.
- Cart Abandonment Rate: The percentage of users who add items to their cart but don’t complete the purchase.
- Conversion Rate: The proportion of users who complete a desired action, such as making a purchase, signing up for a newsletter, or downloading a resource.
4. Customer Retention: Building Long-Term Loyalty
Retention metrics gauge a business’s ability to maintain relationships with existing customers.
- Customer Lifetime Value (CLV): The projected revenue a customer generates over their lifetime, calculated by multiplying the average purchase value, purchase frequency, and customer lifespan.
- Retention Rate: The percentage of customers who continue purchasing over a specific period.
- Churn Rate: The percentage of customers who discontinue their relationship with the business.
5. Advocacy: Turning Customers Into Brand Champions
Satisfied customers often become advocates, driving organic growth through recommendations.
Net Promoter Score (NPS): A customer satisfaction metric based on the question: “How likely are you to recommend our product/service to a friend or colleague?” NPS is calculated by subtracting the percentage of detractors from the percentage of promoters.
Benefits of eCommerce analytics
With eCommerce data analytics, businesses can find areas of opportunity, predict important trends, and gain efficiencies that can let them retain customers and lead to growth.
Produce useful insights for business’s strategies
By analyzing data, you’d see what is happening in-store. For example, by looking at product data, it’s clear what qualities your customers care about the most. Then you can use this information to innovate future products.
Another example is that you want to expand your business to a new region. After analyzing data, you’d be able to decide the possibility and investment for expansion, and allocate the resources.
Understand customers behavior
Do you know how customers navigate your website or at which step they usually left before making a purchase? Which parts of your website attract customers?
Knowing how the customer behaves gives tremendous help when designing your website for the best experience which would make them come back. It comes from the simplest things like where the CTA button should be placed, or images or text would be more suitable for a specific page section, …
Personalize customer experience
By looking at customer demographics, you’d know your target customer better and be clear on what to offer them. People in different age groups have interests in different product categories, or if a product is sold more in a certain region, … all of these can be analyzed for valuable insights.
Then you can work on offering a personalized experience for marketing sales. Delivering the right promotions to your customers will raise their interest and result in more conversions.
Evaluate marketing efforts
Marketing is great, but only when resources are effectively allocated. Collected data helps to evaluate how impactful your marketing strategy or campaign was, if it’s targeting the right segmentation, which channels/campaigns can generate more leads, are people looking at the product you want them to buy,…
By learning from previous campaigns and data, the next marketing strategies can be improved to drive more sales and expand the business.
Optimize pricing
Pricing is an important factor that impacts a person’s purchase decision. Your pricing strategy can set you apart and attract customers when done right. With analytics tools, you get insights into the upcoming changes, thus determining the optimum price for a product to maximize profit.
For example, when it’s almost time for the summer holiday, sales for items like sunglasses or sunscreen increase and they are shown on reports, you may raise the prices accordingly to maximize profits.
Forecast
Ecommerce analytics tools provide important forecasts for how your business will likely perform in the future from the available data. The forecasts can cover almost any aspects from sales, customers, operation costs to product inventory.
Best eCommerce analytics tools 2025
The tools that will help automate e-commerce analytics are used to understand the performance of a business along with customers’ behavior about that specific business. Here are the top five eCommerce analytics tools and their features:
- Google Analytics: A web analytics giant that adjudges traffic, user behavior, and conversion rates across time. It thus assesses organic search impressions, sessions, bounce rate, and revenue from organic traffic. Some of the most important metrics that are monitored include returning visitors, time on site, and pages viewed per visit.
- Shopify Analytics: Provides built-in dashboards and reports to see everything related to sales, customer buying behavior, and marketing.
- BigCommerce: Has ways to analyze data on the online store integrating with data warehouses and BI tools which would be a one-stop view of business operations and customer journeys.
- Adverity: Specializes in data integration and visualization. It imports data from different sources, cleans it, and creates display dashboards for further actionable insights.
- ThoughtSpot: An AI-powered analytics platform that offers natural language search and interactive dashboards. Inventory optimization, marketing strategy, and, of course, sales can be performed through it.
Tips for tracking your eCommerce performance
- Monitor regularly: This advice sounds obvious, but many businesses are not doing enough monitoring. After getting your analytics tools set up, it’s better to check the data regularly. Depending on the kind of data, the frequency can be daily, weekly or monthly.
- Set goals: With tools like Google Analytics, you can set goals for better data tracking like “click to main site”, “purchase product”, or “leads”. This will also let the analytics tools produce more useful reports to match your business demands.
- Investigate consumer journey: Crazy Egg or Hotjar are popular tools that help understanding how a customer interacts with your website. By understanding the customer journey, your online store can be optimized for purchases.
- Set up custom alerts: Website errors impact customer experience negatively, and that’s not good for your online store. So we suggest setting up custom alerts for cases like server or link errors.
- A/B testing: To decide which strategies work for your business and customers, A/B testing is a great option. You can opt for small changes like reallocating a few elements on your websites or switching promoted products, … These changes may improve your content, drive higher conversion rates, and improve user engagement.
- Make the most out of your eCommerce platform: Your eCommerce platform often comes with useful analytics tools, such as Magento BI, Shopify Analytics or Bigcommerce Analytics. However, these analytics tools are sometimes overlooked by businesses, but they are just as strong as other third-party tools.
The data is generated directly from your store, and the reports are updated on time. These platforms can provide diverse reports to cover aspects of your eCommerce business.
FAQs
What is eCommerce performance analytics?
E-commerce performance analytics involves measuring and analyzing key metrics related to customer behaviors and sales conversions within an online store. This analysis helps businesses understand what actions lead to conversions, identify high-performing segments, and optimize marketing strategies to improve overall performance.
What is data analytics in commerce?
Data analytics in commerce refers to the systematic collection, analysis, and interpretation of data related to sales, customer behavior, and market trends. It enables businesses to gain insights that inform decision-making, optimize operations, and enhance customer experiences.
How big data is used in e-commerce?
Big data in e-commerce is utilized to analyze vast amounts of customer information, transaction records, and market trends. This analysis helps businesses personalize marketing efforts, predict customer behavior, optimize inventory management, and improve overall operational efficiency.
What is the role of a data analyst in eCommerce?
A data analyst in eCommerce is responsible for collecting, processing, and analyzing data to derive actionable insights. They help identify trends in customer behavior, measure the effectiveness of marketing campaigns, and provide recommendations for optimizing sales strategies and improving user experiences.
What is e-commerce analytics software?
E-commerce analytics software is a tool designed to collect, analyze, and visualize data from online stores. It provides insights into customer behavior, sales performance, and marketing effectiveness through dashboards and reporting features, enabling businesses to make informed decisions based on real-time data.
Final thoughts
Incorporating eCommerce analytics into your business process takes time and effort, but the results will no doubt pay off if it’s done correctly. It’s not always simple with all the tool integration and learning and analyzing stages that follow.
To start off, we would recommend some tools like Google Analytics, KISSmetrics, Optimizely, Crazy Egg, … These tools provide more than enough for beginners, and you can advance your game with them as time passes.
