Last Updated

May 2, 2023

Introduction to E-commerce Analytics and its Significance in the Online Retail Industry




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14 minute

Are you an online retailer looking to improve your business? As e-commerce grows, tracking and analyzing data to make informed decisions is essential.

SYSINT has written an informative article covering the importance of e-commerce analytics and how to track key performance indicators (KPIs) using Google Analytics. Discover the top five KPIs for online retailers and learn how to follow them easily.

By utilizing e-commerce analytics, you can gain valuable insight into your customers' behavior, preferences, and buying patterns to improve your products/services.

With two analytics categories, descriptive and predictive, you can gain insights into past events and predict future outcomes based on historical data analysis. Use both types to enhance customer experience and personalize the shopping experience for your customers.

How can e-commerce analytics be used to improve a business?


Identify popular products.

By analyzing product data, online retailers can identify which products are selling well and adjust their inventory, marketing, and pricing strategies accordingly. For example, suppose analytics show that a particular product is consistently popular. In that case, the retailer may choose to increase the inventory of that product, create targeted marketing campaigns, or offer discounts to encourage customers to purchase it.

Optimize the sales funnel.

Analytics can help online retailers identify where customers drop off in the sales funnel, such as at the checkout page or during the product selection. By understanding where customers are abandoning their purchases, retailers can improve their website design, user experience, or pricing strategies to encourage customers to complete their purchases.

Improve customer segmentation.

By analyzing customer data, online retailers can segment their customer base into groups based on demographics, purchase behavior, and other factors. This information can help retailers tailor marketing campaigns and product offerings to specific customer segments, improving their overall customer experience and increasing customer loyalty.

Evaluate marketing campaigns.

By tracking the performance of marketing campaigns, online retailers can identify which campaigns drive the most traffic and revenue to their websites. This information can help retailers optimize advertising spending and adjust marketing strategies to improve their ROI.

Optimize pricing strategies.

By analyzing pricing data, online retailers can identify trends and patterns in customer behavior, such as which products customers are most likely to purchase at different price points. Retailers can optimize their revenue and profitability by using this information to adjust pricing strategies.

With the help of e-commerce analytics, online retailers can gain essential insights into their business performance and make informed decisions based on data to enhance their operations.

How do I easily track eCommerce data in Google Analytics reports?

You'll need to set up a Google Analytics account to get started. This free service can track your website or app's performance. After creating an account and verifying your site's ownership, you can begin monitoring eCommerce data.

Google Analytics has three main types of reports: Audience (who is visiting), Acquisition (how they found us), and Behavior (what they do when they get here). Each report has the purpose of helping businesses understand how their customers interact with their websites or apps.

What are the five most important KPIs for an online retailer?

Key performance indicators (KPIs) are metrics that help you measure the success of your business. They can track individual campaigns, products, or customers and help you understand what's working and what isn't.

The five most important KPIs for an online retailer are:

  • Conversion rate - The percentage of visitors who take a specific action on your website, such as buying a product or subscribing to a newsletter.
  • Average order value - the average amount spent per customer during one purchase;
  • Shopping cart abandonment - how many people add items to their cart but don't check out;
  • Revenue on advertising spend - how much revenue comes from each dollar spent on advertising;
  • Customer lifetime value - the total revenue a customer generates over their entire relationship with your business.

Conversion Rate

The conversion rate is one of the most critical metrics for any ecommerce site. It tells you how many people are completing a desired action on your site, such as purchasing an item or signing up for a newsletter. A high conversion rate shows that your website performs well and converts visitors into customers.

To calculate your conversion rate, divide the number of conversions by the total number of visitors to your website. For instance, if you have 100 visitors and ten converts, your conversion rate is 10%.

Here are some steps you can take to understand your conversions better:

1. Analyze website traffic.

Analyze your website traffic to see where your visitors are coming from, which pages they visit, and how long they stay on each page. This information will help you identify areas of your website that need improvement.

2. Analyze user behavior.

Use analytics tools to analyze user behavior, such as click-through rates, bounce rates, and time spent on your website. This information will help you understand how users interact with your website and identify any obstacles preventing them from converting.

3. Identify conversion barriers.

Identify barriers preventing users from converting, such as a complicated checkout process, slow loading times, or lack of trust signals. Once you identify these barriers, you can remove them and improve your conversion rate.

4. Test and optimize.

Test different elements of your website, such as the design, copy, and calls to action, to see what works and what doesn't. Use A/B testing to compare different website versions and optimize for conversion rate.

*Average Order Value

As an e-commerce business owner, understanding and analyzing your average order value (AOV) is crucial to success. AOV is the average amount of money a customer spends per order, and it's one of the most critical metrics for measuring your business's performance.

Here are some steps you can take to analyze your AOV:

1. Calculate your AOV.

To calculate your AOV, divide your total revenue by the number of orders over a specific period. For example, if you made $10,000 in revenue from 100 orders monthly, your AOV would be $100.

2. Segment your data.

Analyzing your AOV by different segments can help you identify patterns and opportunities. Consider segmenting your data by product, customer type, traffic source, or period.

3. Look for trends and patterns.

Analyze your AOV over time to identify any trends or patterns. Are there certain times of the year when your AOV is higher or lower? Do certain products or customer segments have a higher AOV?

4. Identify opportunities.

After recognizing patterns and trends, seek ways to enhance your average order value (AOV). You can achieve this by bundling products, providing discounts for bulk orders, or recommending additional products to customers.

5, Monitor your progress.

Keep track of your AOV over time and monitor the impact of any changes you make. By utilizing this tool, you can accurately distinguish between what is successful and what is not and proceed with any required modifications.

Shopping Cart Abandonment

Shopping cart abandonment is a significant challenge for e-commerce businesses. It occurs when a customer adds products to their shopping cart but leaves the website without completing the purchase. Analyzing shopping cart abandonment can help you identify where customers drop off and improve your website and checkout process.

Here are some strategies to help you better handle shopping cart abandonment:

1. Calculate your abandonment rate.

Calculating your shopping cart abandonment rate is crucial. This metric determines the percentage of customers who have added products to their cart but still need to complete their purchase.

For example, if you had 1,000 shopping carts created and 500 completed purchases in a month, your abandonment rate would be:

((1,000 - 500) / 1,000) x 100 = 50%

2. Identify common drop-off points.

If you want to enhance your website's checkout process, it's crucial to examine your analytics and pinpoint where customers are leaving their purchases. This could happen on various pages, such as the shopping cart page, the shipping or payment information page, or even the order confirmation page. Once you identify these areas, you can make the necessary changes to improve the overall checkout experience for your customers.

3. Analyze customer behavior.

Consider the behavior of customers who abandon their carts. Are they new customers, or have they made purchases before? Are they browsing on mobile or desktop devices? This information can help you identify specific customer segments to target with improvements.

4. Make improvements.

Based on your analysis, improve your website and checkout process. This could include simplifying the checkout process, offering guest checkout, adding trust signals such as security badges or customer reviews, or retargeting customers with abandoned cart emails.

Monitor your progress.

Regularly monitor the impact of your improvements on your shopping cart abandonment rate. This will help you determine what's working and what's not and adjust as needed.

Your shopping cart abandonment rate is an important metric to track and improve over time, as it can significantly impact your e-commerce business's revenue and profitability.

By analyzing shopping cart abandonment, you can identify areas for improvement in your website and checkout process, ultimately reducing the number of customers who abandon their carts and increasing your conversion rate.

Revenue on Advertising Spend

As an e-commerce business owner, it's crucial to understand how your advertising spend is impacting your revenue. You can optimize your advertising budget and improve your ROI by analyzing your income on advertising spend.

Here's how to do it:

1. Calculate your ROI.

Once you have your revenue and advertising spending data, you can calculate your ROI. Divide your revenue by advertising spend and multiply by 100 for a percentage. A positive ROI means you're making a profit, while a negative ROI means losing money.

2. Analyze your data.

Look for patterns in your data to identify which advertising campaigns drive the most revenue. Analyze your ROI by channel, campaign, and ad type to determine where to focus your advertising budget.

3. Optimize your campaigns.

Use your data analysis to optimize your advertising campaigns. Shift your budget towards the campaigns driving the most revenue and adjust your ad targeting and messaging to improve performance.

Customer Lifetime Value

Analyzing customer lifetime value (CLV) is essential to e-commerce analytics. It refers to the total amount of money a customer is expected to spend with your business throughout their lifetime. Analyzing CLV can help you understand the long-term value of your customers and make data-driven decisions about marketing, customer retention, and more.

Here are the steps to analyze customer lifetime value:

Calculate your CLV.

To calculate CLV, you must determine the average purchase value, the intermediate purchase frequency, and the average customer lifespan. Multiply these values together to get your CLV.

The formula looks like this:

CLV = Average Purchase Value x Average Purchase Frequency x Average Customer Lifespan

For example, if the average purchase value is $50, the average purchase frequency is two times per year, and the average customer lifespan is three years, the CLV would be $300.

Segment your customers.

Segment your customer base into groups based on their CLV to identify high-value, low-value, and everything in between. This information can help you prioritize your marketing efforts and allocate resources accordingly.

Analyze customer behavior.

Look at customer behavior to identify trends and patterns that can help you improve CLV. For example, do high-value customers purchase certain products or use certain features more frequently than low-value customers? Use this information to optimize your marketing and sales strategies.

Improve customer retention.

Improving customer retention is one of the most effective ways to increase CLV. Use your analysis to identify areas where you can improve the customer experience and develop strategies to improve retention.

Monitor and adjust.

CLV is not a static metric. It changes over time as customer behavior and market conditions change. Continuously monitor your CLV and adjust your strategies to ensure long-term success.

By analyzing customer lifetime value, you can gain valuable insights into your customer base and make data-driven decisions that can help you increase revenue and grow your business.

Analytics can help you understand your customers' needs and wants, what they value, and how to improve your service. It also allows you to measure the success of your efforts and make data-driven decisions that will improve sales and optimize customer experience.

Use data to create a successful marketing strategy. Connect with your target audience through tailored content, provide offers at the right time, and track engagement levels to improve messaging and content. Adjust based on actual performance.

SYSINT can help with e-commerce analytics through our Data Business Analytics service. This service allows businesses to analyze their KPIs, metrics, and goals in one place with the Elastic Stack and create and share reports with real-time data.

Therefore, if you need help with e-commerce analytics, we can provide the tools and expertise to analyze your business data and make informed decisions.

Contact us!


What are the other KPIs that online retailers should track using Google Analytics?

Online retailers can track various key performance indicators (KPIs) using Google Analytics to measure the success of their e-commerce website. Here are some of the specific KPIs that online retailers should track as well:

  • Customer acquisition cost: This is the cost of acquiring a new customer, including advertising, marketing, and sales expenses. Tracking CAC can help you optimize your marketing budget and improve ROI.
  • Revenue by traffic: The revenue by traffic source metric indicates the income generated from each traffic source, such as organic search, paid search, social media, and email marketing. By keeping track of revenue by traffic source, you can determine which channels are the most effective in boosting sales.
  • Return on investment: This metric measures the return on your marketing investment, showing how much revenue is generated for each dollar spent on advertising. Tracking ROI can help you optimize your marketing budget and improve profitability.

By tracking these KPIs and others, online retailers can gain valuable insights into the performance of their e-commerce websites and make data-driven decisions to improve revenue, profitability, and customer satisfaction.

What specific data should be collected and analyzed through e-commerce analytics?

Several types of data can be collected and analyzed through e-commerce analytics, including:

  • Customer behavior data : This includes data on how customers interact with your website, such as the pages they visit, the products they view and add to their cart, and how long they spend on your site.
  • Sales data includes the number of sales made, the total revenue generated, and the average order value.
  • Marketing data: This includes data on the effectiveness of your marketing campaigns, such as the number of clicks, conversions, and revenue generated from each campaign.
  • Inventory data: This includes data on your inventory levels, such as the number of products sold, the number of products in stock, and the reorder point.
  • Customer data: This includes data on your customers, such as their demographics, purchase history, and customer lifetime value.

By analyzing this data, you can gain insights into your customers' behavior, preferences, and buying patterns, which can help you optimize your e-commerce platform, improve your customer experience, and boost sales.

How can retailers effectively use descriptive and predictive analytics to inform business decisions?

Retailers can effectively use both descriptive and predictive analytics to inform business decisions by following these steps:

  • Determine the business question: The first step is determining the business question that needs to be answered. This could be related to inventory management, customer behavior, or sales forecasting.
  • Gather data: Once the business question has been determined, the next step is to gather data. This data can come from various sources, including sales records, customer demographics, and website analytics.
  • Analyze data: After collecting the data, it needs to be analyzed. Descriptive analytics can understand past events, while predictive analytics can forecast what will likely occur.
  • Develop insights: Once the data has been analyzed, retailers can develop insights that inform business decisions. For example, suppose the data shows that customers are more likely to purchase a product when it is displayed at eye level. In that case, a retailer may adjust their store layout accordingly.
  • Implement changes: Finally, retailers can implement changes based on their developed insights. This could involve changing product displays, adjusting pricing strategies, or optimizing inventory management.

By using descriptive and predictive analytics, retailers can gain a more comprehensive understanding of their business and make more informed decisions that drive growth and profitability.

What are some specific examples of how e-commerce analytics can improve customer experience and operational efficiency?

E-commerce analytics can provide insights into customer behaviors, preferences, and pain points, which can help businesses make data-driven decisions to improve customer experience and operational efficiency. Here are some specific examples:

  • Chatbot analytics: By analyzing historical conversations on e-commerce chatbots, companies can gain insights into customer experience, the overall performance of the chatbot itself, and future performance. [1]
  • Voice search: AI can improve the accuracy and relevance of voice search results, which is becoming increasingly popular among customers. [2]
  • Customer journey analytics: Businesses can uncover the cause of new client service calls and increase operational efficiency by analyzing customer journey data. For example, a retail bank uses customer journey analytics to understand the effectiveness of its self-service channels when customers have a problem making a mortgage payment. [3]
  • Technologies and cloud-based communication solutions: Businesses can use technologies and cloud-based communication solutions to connect the dots to fix broken customer experiences, which can also improve ROI, enhance conversion rate, increase shopper loyalty, improve productivity, and reduce expenses. [4]
  • AI-driven approaches: AI-driven approaches, such as data science, extended reality, robots, recommender systems, the internet of things, and conversational agents, can improve customer experience.[5]




May 2, 2023

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