By: Michael Keenan
What is E-commerce Analytics?
E-commerce analytics is the collection and analysis of data from an online store to make business decisions. These analytics track metrics such as sales, customer behavior, and website performance, providing insights to improve marketing strategies, enhance customer experience, and increase overall profitability.
Benefits of E-commerce Analytics
E-commerce analytics help focus and manage data. Siva K. Balasubramanian, Dean of the Stuart School of Business at Illinois Institute of Technology, explains that the emergence of multiple data sources for collecting and integrating data about customers, products, and markets is a common problem for companies today. Balasubramanian states, “Analytics provide useful techniques to address this issue by organizing data to develop metrics that are most useful for continuously monitoring business performance.” He adds, “The focus of analytics is on the issues that matter most to the business, and performance metrics are useful in identifying and resolving problems in real-time.”
Types of E-commerce Analytics
There are many analytical indicators you can track, but if you are a beginner in your journey as an e-commerce business owner, this is the right place to start. There are five metrics you can objectively monitor to ensure your store avoids the issues faced in the example above and grows over time:
Customer Lifetime Value (CLV)
This is the amount you will earn from the average customer over their time as a customer. For instance, if the average customer returns to your store three times to buy something, spending an average of $100 per purchase, and your profit margin is 10% ($10), then the CLV for that customer is $30.
Return Visitor Rate
This is the percentage of users who return to your site after their first visit. This figure is a clear indicator that people were impressed with what they saw.
Time Spent on Site
This is the average time users spend on your site during each visit. If people are spending time on your site, it means they are enjoying a good customer experience.
Pages per Visit
This is the average number of pages users navigate on your site in a single visit. A high number of pages per visit (around four) indicates that people are interested in what you are selling.
Bounce Rate
This is the percentage of users who visit only one page on your site and leave before taking any action. A high bounce rate usually means that your site is not making a good impression initially. This can be due to poor design, unmet expectations, or slow page load times.
Tips for Successful E-commerce Analytics
1. Set your goals in advance: Before engaging in analytics, you should clearly define your objectives and those of your team. Marketing goals should align with overall business objectives.
2. Create benchmarks: Establishing benchmarks is important for comparing data and setting meaningful goals. Leveraging benchmarks provides valuable context and helps you set meaningful targets and understand how you compare over time.
3. Optimize your campaigns: After analyzing the data, you can optimize your campaigns by gradually adjusting marketing variables to make them more relevant or optimal.
4. Incorporate data into the company routine: You can see the difference in performance for companies that integrate data into their weekly routine. Regularly analyzing data and gaining marketing insights from analytics and applying those insights are the key factors for becoming most successful.
5. Set goals in advance: Before engaging in analytics, you should clearly define your objectives and those of your team. Marketing goals should align with overall business objectives.
Common Challenges in E-commerce Analytics
There are
Some challenges you may face when analyzing data in the e-commerce field:
1. Data inconsistency: Gathering data from different sources can make analysis difficult. It may require unifying the data on a single platform to understand it and take data-driven actions.
2. Data privacy: Ensuring the privacy and security of data is a must. Secure data storage and conducting regular compliance checks are necessary to ensure ongoing protection.
3. Data quality: Poor-quality data, such as incorrect, incomplete, or outdated information, can lead to poor decision-making. Relying on inaccurate sales data can lead to overstocking or understocking products and negatively impact profitability.
4. Selective data picking: Selectively picking data means focusing on data points that support a particular conclusion while ignoring or excluding other relevant data. This can lead to focusing only on a successful sales week for a winter coat while ignoring the overall decline throughout the season.
Using the best e-commerce analytics tools to enhance your store
Most businesses do not fail due to a lack of effort or dedication, but rather fail due to implementing the wrong things. The trick is to understand the important data points at each development stage and use this knowledge to make changes that will genuinely impact your business’s bottom line.
Use Shopify’s built-in reports and analytics to make more focused and quicker decisions. Choose from over 60 pre-built dashboards and reports, or customize your dashboard to discover trends, capitalize on opportunities, and enhance your decision-making process.
Frequently asked questions about e-commerce analytics
What are the most common types of data in marketing analytics?
The most common data in marketing analytics includes customer data, competitive information, market research, transactions, customer feedback, and preferences and interests.
What are the benefits of e-commerce analytics?
E-commerce analytics can solve common business problems, such as reducing revenue models and misleading forecasts. It also helps you understand marketing data, discover trends, use customer data, and optimize pricing.
How do marketers use analytics to make decisions?
Big data and business analytics can help predict consumer behavior, determine the return on investment for your marketing activities, understand marketing attribution, and improve the decision-making process.
Source: https://www.shopify.com/blog/ecommerce-analytics-for-beginners
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