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. This analytics tracks 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 helps to focus and manage data. Seva K. Palasubramanian, Dean and Professor of Marketing at the Stuart School of Business at the University of Illinois, explains that the availability of multiple data sources to collect and integrate data about customers, products, and markets is a common issue in modern businesses. Palasubramanian states, “Analytics provides useful techniques to address this issue by organizing data to develop metrics that are most helpful in continuously monitoring business performance.” Analytics focuses on issues that significantly concern businesses, and performance metrics are useful in identifying and solving problems in real-time.
Types of E-commerce Analytics
There are many analytics indicators you can track, but if you’re a beginner in your journey as an e-commerce entrepreneur, this is the right place to start. There are five metrics you can objectively follow to ensure your store avoids the issues faced by the group in the example above and grows in a timely manner:
1. Customer Lifetime Value (CLV): This is the amount you will earn from the average customer over the duration of their time as a customer. For example, if the average customer comes back to your store three times to purchase something and spends an average of $100 on each purchase, and your profit margin is 10% ($10), then the CLV for that customer is $30.
2. Returning Visitors: This is the percentage of users who return to your site after their first visit. This number is a clear indicator that people liked what they saw.
3. 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.
4. Number of Pages per Visit: This is the average number of pages users navigate through on your site during a single visit. A high number of pages per visit (about four) indicates that people are interested in what you are selling.
5. Bounce Rate: This is the percentage of users who visit only one page on your site and leave without taking any action. A high bounce rate (usually over 57%) means that your site is not making a good impression at first glance. This can be due to poor design, failure to meet expectations, or slow page loading.
Tips for Successful E-commerce Analytics
1. Set Your Goals in Advance: Before diving into analytics, you should define your goals and your team’s objectives. Marketing goals should be linked to overall business goals.
2. Create Benchmark Metrics: Creating benchmark metrics is important for comparing your performance against your past and achieving your goals. This provides you with valuable data and helps you set meaningful goals and see how you compare to yourself over time.
3. Optimize Your Campaigns: Analytics focuses on measuring business performance and the variables that help achieve that performance. Optimization processes can help enhance performance by gradually adjusting marketing variables to be more suitable or optimal.
4. Integrate Data into Company Routine: You can see the difference in performance among businesses that integrate data into their weekly routine. Successful companies are those that analyze data, gain marketing insights from their analytics, and put those insights into practice.
Challenges
The Buzz in E-commerce Analytics
There are some challenges you may face when analyzing data in the e-commerce field:
1. Data inconsistency: Aggregating data from different sources can make analysis difficult. Each platform may provide data in different formats and standards, making it hard to unify the information for comprehensive analysis. You can funnel all the data to a single platform and format it correctly to understand and work with it.
2. Data privacy: Data privacy and security must be ensured. Failing to do so can lead to legal consequences and break trust with customers. Make sure to use secure data storage and conduct regular compliance checks to ensure ongoing protection.
3. Data quality: Poor quality data, such as incorrect, incomplete, or outdated information, can mislead your decisions. Imagine making inventory decisions based on inaccurate sales data. You would either end up with overstock or product shortages, negatively impacting profitability.
4. Selective data sampling: Selective data sampling means focusing on data points that support a specific conclusion, while ignoring or excluding other relevant data. Imagine you run an online clothing store and analyze sales data to decide which products to promote. You might select certain data that focuses only on a successful sales week for winter jackets, ignoring the overall decline throughout the entire season.
Using the Best E-commerce Analytics Tools to Improve Your Store
Most businesses fail not due to a lack of effort or dedication, but because of implementing the wrong things. The trick is to understand the important data points for each stage of development and use that knowledge to make changes that will actually impact the bottom line.
Use Shopify’s built-in reports and analytics to make smarter and faster decisions. Choose from over 60 ready-made dashboards and reports, or customize your own reports to uncover trends, capitalize on opportunities, and enhance your decision-making process.
Are you ready to start your business? Begin your free trial of Shopify – no credit card required.
Frequently Asked Questions About E-commerce Analytics
What are the common types of data in marketing analytics?
Common data in marketing analytics includes customer data, competitive information, market research, transactions, customer feedback, preferences, and interests.
What are the benefits of e-commerce analytics?
E-commerce analytics helps solve common business problems, such as reducing revenue models and misleading forecasts. It also assists in understanding marketing data, uncovering trends, leveraging customer data, and optimizing 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 decision-making processes.
Source: https://www.shopify.com/guides/ecommerce-analytics/customer-acquisition-metrics
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