Ecommerce Analytics: How to Analyze Data & Extract Insights

The e-commerce industry continues to grow at remarkable rates, catering to varying consumer needs and preferences. Based on a report by Emarketer, global e-commerce sales are expected to increase from $6.091 trillion in 2024 to $8.092 trillion by 2028.

However, e-commerce businesses must constantly analyze data to extract actionable insights and stay competitive. From understanding customer behavior and optimizing operations to increasing sales, e-commerce analytics can help your business thrive.

Let’s look into the details of e-commerce data analytics, the key metrics to track, the associated challenges, and some of the best analytical tools.

What is E-commerce Analytics?

E-commerce analytics is the collection, analysis, and interpretation of data from online stores to make informed business decisions. It involves tracking metrics, including sales, marketing, customer behavior, website traffic, and key performance indicators (KPIs).

With the gathered metrics, you can gain insights into your store’s performance and identify areas for improvement. This will lead to data-driven decisions to optimize online sales and marketing efforts.

How Important is Data Analytics for E-commerce?

Data analytics in e-commerce can help you understand trends and patterns in the data. This will help you make better decisions and become more agile and customer-centric.

With e-commerce data analytics, you can:

• Optimize pricing and inventory performance.
• Discover trends for accurate forecasting.
• Measure the effectiveness of your marketing campaigns.
• Gain improved insights into customer behavior and preferences.
• Use data-driven insights to enhance marketing strategies.
• Refine the user experience of your online store.
• Enhance customer retention and loyalty.

These capabilities enable you to have a competitive advantage over other businesses. You can swiftly adapt to market changes by constantly analyzing customer feedback, monitoring performance metrics, and staying up-to-date on market trends.

Challenges With E-commerce Analytics

There are certain challenges that e-commerce businesses may encounter with regard to data analysis. Here are some of the common ones:

• Data Quality: Issues in data quality include incomplete, incorrect, or outdated information. Such poor-quality data impacts the accuracy of analysis and insights, resulting in misguided decisions.‍
• Varied Data: The data considered for e-commerce analytics is collected from different sources, such as Google Ads, Facebook Ads, and email marketing. This data is available in various formats and types. As a result, it becomes difficult to consolidate the information for effective analysis.‍
• Data Privacy and Security: E-commerce businesses must ensure appropriate safeguards to protect customer data. This involves compliance with regulations such as GDPR to help avoid legal consequences and maintain trust with customers. You must also secure data storage and have regular compliance checks.‍
• Data Volumes: E-commerce businesses often generate huge volumes of data, including marketing campaign performance, product information, customer behavior, purchase history, and logistics data. It can be challenging, particularly for smaller businesses with limited resources, to manage and analyze such vast data.‍
• Integration of Data: Manually integrating e-commerce data from sources such as CRM systems, social media, and email marketing tools for analytics can be time-consuming and effort-intensive. With tools like Airbyte, you can automate the entire process of data integration, centralizing the data for effective analysis and reporting.

Essential E-commerce Analytics Metrics You Should Track

There are some critical e-commerce analytics metrics that you must track to optimize performance and drive sales. Here are the different metrics that can provide you with business insights, helping with data-driven decision-making:

Sales Metrics

• Average Order Value (AOV): It is the average amount a customer typically spends in a single purchase. Higher AOV values signify good customer trust and spending ability.‍
• Total Sales: In total sales, it is essential to understand your gross sales, which is the total revenue without any deductions. The other element is net sales, which is the gross sales minus returns, discounts, and sales allowances. With these estimates, you can get a comprehensive overview of your business performance for better forecasting decisions.‍
• Cart Abandonment Rate: It is the percentage of visitors who add items to their cart but don’t complete the purchase. A high abandonment rate indicates problems with the checkout process or that customers are finding better deals elsewhere.‍
• Sales Conversion Rate: This translates to the percentage of website visitors who make a purchase. Tracking conversion rates can help you understand your website’s effectiveness in converting visitors to customers.

Marketing Metrics

• Click-Through Rate (CTR): CTR is the percentage of people who click on a link after viewing your social media post, landing page, or ad. This is calculated by considering the total number of people who view the ad or page.‍
• Traffic: This metric is useful to track the total number of visitors to your website. It also includes information about where the visitors come from and which pages they view. With this, you can gauge how well your marketing efforts drive people to your site.‍
• Return on Advertising Spend (ROAS): ROAS allows you to measure your ad campaign’s effectiveness by comparing the generated revenue and the amount spent on advertising.‍
• Impressions: This shows how many times viewers see an ad or piece of content, regardless of whether they click on it or not.

Customer Metrics

• Customer Lifetime Value (CLV): The CLV provides an estimate of how much a customer may spend on your website over their lifetime as your customer. This will help you determine the amount to invest in customer acquisition and retention efforts.‍
• Customer Acquisition Cost (CAC): CAC represents the total expenses incurred in acquiring a single customer. This allows you to understand how much you’re spending to attract new customers. A lower CAC implies you are acquiring customers at a reasonable cost relative to your revenue generation.‍
• Net Promoter Score (NPS): This metric helps measure customer loyalty and satisfaction. It involves determining how likely they are to recommend your products or services to others.‍
• Customer Retention Rate: The customer retention rate enables you to gauge how well your business retains its customer base over a specific period. High retention rates indicate customer satisfaction and returning customers.

Website Metrics

• Page Load Time: This crucial website performance metric indicates how long it takes for a single webpage to load. Fast-loading pages will result in superior user experiences, increasing visitor interactions with your site. According to Google, if page load time increases from one to three seconds, it results in an increase in bounce probability by 32%.‍
• Time on Site: The average duration of a single session per user converts to time on site. Users who spend longer on the website often find it engaging and user-friendly and are likely to make purchases. However, a website bounce occurs when users leave a page within 10 seconds.‍
• Returning Visitors: This is the number of users visiting a website or platform more than once in a specific timeframe, demonstrating continued interest, trust, and loyalty to the brand.‍
• Pages Per Visit: The pages per visit metric is a measure of the average number of pages a visitor views in a single website session. A higher average shows the user’s interest in exploring and engaging with the website content or offerings.

E-commerce Data Analysis Techniques

There are many e-commerce data analysis techniques, each serving a specific purpose, helping you understand and improve your online operations.

• Descriptive Analysis: This analysis is useful to understand customer behavior and preferences. The focus is on summarizing past performance data for insights. Common metrics include website traffic, sales revenue, customer demographics, and conversion rates. With this, you can measure performance and identify trends and customer buying patterns.‍
• Predictive Analysis: With predictive analytics, you can forecast future customer behavior and trends. It involves utilizing historical data and statistical algorithms to forecast customer lifetime value, market trends, and product demands, among others. By anticipating customer behavior, you can make proactive decisions and capitalize on opportunities.‍
• Prescriptive Analysis: This involves utilizing advanced algorithms and operational techniques to help you identify effective strategies for minimizing costs, maximizing revenue, and enhancing operations.‍
• Sentiment Analysis: Sentiment analysis is determining the sentiment or emotional tone by analyzing text data from social media mentions, customer reviews, and other such sources. This allows you to gauge customer satisfaction, effectively respond to customer feedback, and identify areas for improvement.‍
• Churn Analysis: With this analysis, you can identify customers who have quit making purchases or stopped engaging with your business. Churn analysis enables you to understand why customers are leaving; you can implement targeted retention strategies to boost customer lifetime value and lower churn rates.

How to Perform E-Commerce Data Analytics?

Here are a few steps that you can follow to effectively perform e-commerce enterprise data analytics:

Data Collection

The first step in e-commerce data analytics is to collect data from different sources. Commonly collected data includes product performance, website traffic, customer purchase histories, and customer interactions across various platforms.

Data Cleaning

After collecting the data, you must clean and prepare it for analysis. This involves operations such as correcting errors, removing duplicates, and handling missing or incomplete information. Data cleaning will provide you with a reliable dataset for valuable insights.

Data Integration

The next step is to integrate data from multiple sources for a combined view of your e-commerce business. You can combine the data into a central data store like a data warehouse. Following this, you can access this data for analysis to gain valuable insights.

Airbyte can help simplify your e-commerce data integration process. With over 550+ configurable connectors, it takes only a few minutes to move data between sources and destinations with Airbyte. However, if the connector of your choice isn’t available, Airbyte offers the following options:

• A no-code Connector Builder with an AI assistant
• A low-code Connector Development Kit (CDK)
• Several language-specific CDKs

Data Analysis

You can use analytical tools and algorithms to discover trends and patterns from the data in your destination (warehouse).

‍Data analysis tools such as Power BI, Tableau, and Looker are useful for visual representations of your data in the form of charts, graphs, etc. These tools make analysis easier and allow you to present your findings to your stakeholders effectively.

Few Best E-commerce Analytics Tools

The right e-commerce analytics tools can help you make data-driven decisions for the optimization of your e-commerce business and drive sustainable growth. This can boost your conversion rate and improve your customers’ journey.

Let’s look at some of the best e-commerce analytics tools you can use:

• Google Analytics (GA): This is a popular data analytics tool that offers a wide range of essential features for free. With the Google Analytics Enhanced E-commerce feature, you can achieve advanced analytics. GA is useful for measuring customer engagement and bounce rates, how each product is performing on your site, and where the traffic is coming from, among other things.‍
• Kissmetrics: Kissmetrics is a premium analytics solution that is well-suited if you require a wide range of metrics. It helps e-commerce marketers track customer-specific data so you can view individual customer actions. Kissmetrics analysis enables you to remarket your brand and make way for an audience that can add to your ROI.‍
• Hotjar: This is a heat mapping e-commerce analytics tool that allows you to see how your users interact with your site using heatmaps. You can also improve your conversion rates and conversion tracking by creating real-time recordings to see how visitors browse your whole store.‍
• Optimizely: A website optimization platform, Optimizely allows you to test, personalize, and optimize your e-commerce websites. It supports A/B testing to test variations of your website elements, including product descriptions and call-to-action buttons. With its website personalization capabilities, Optimizely helps you improve customer experience.‍
• Glew: Larger e-commerce stores with 10-20 different marketing channels can benefit from Glew.io, an all-in-one multichannel e-commerce analytics software. It enables you to track KPIs and metrics, analyze user behavior, and measure the performance of customer acquisition channels.‍
• Mixpanel: This is a web and analytics platform offering a free plan for up to 20 million monthly events. Data analysts, UX designers, marketing and product teams, as well as e-commerce managers and executives within the e-commerce sector, can benefit from Mixpanel.‍
• Woopra: Woopra is an advanced e-commerce analytics and customer journey software. It helps you collect data on every customer interaction with your store, protecting your customers’ orders from getting abandoned. You can use Woopra to build real-time customer profiles across all touchpoints and find your most engaged customers.

Questions E-commerce Analytics Can Answer

Analytics in e-commerce can provide you with insights into many aspects of your business and help you answer some important questions, such as:

‍Who are my most valuable customers?‍

By analyzing CLV, AOV, purchase patterns, preferences, and frequency of purchases, you can identify your most valuable customers. When you can determine customers who help generate more revenue, you can tailor your marketing strategies to engage them effectively for longer retention.

‍What products drive the most profit?‍

With e-commerce analytics, you can analyze sales volume, customer satisfaction rates, and future sales projections. This will help you identify which products drive the most profit and deserve continuous investment.

‍Which channels deliver the best ROI?‍

E-commerce analytics can help determine which channels deliver the best ROI by allowing you to track and analyze KPIs and metrics. You can track the conversion rates, CTRs, and traffic to identify the channels that contribute the most ROI.

Conclusion

E-commerce analytics is crucial to drive sales and improve overall business performance. The gathered insights can also help optimize your marketing efforts and boost conversion and retention rates.

By tracking essential sales, marketing, customer, and website metrics, you can gain crucial information for informed decision-making. The choice of appropriate techniques, like descriptive, predictive, or prescriptive, and the right tool, like GA, Kissmetrics, or Hotjar, can simplify your e-commerce analytics.