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What is E-Commerce Analytics?

11 min to read

E-commerce analytics is the process of collecting data, processing, and reporting on the store to understand customer purchasing habits and improve business with the use of analytics tools.

This method enables decisions grounded in available data, rather than decisions based on guesswork.

Key Takeaways:
  • Business decisions can be influenced by data analysis
  • Track key metrics

    • Focus on important numbers like conversion and customer value.

  • Tools have varying accessibility

    • Begin with freely available tools and adapt as needed.

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What is E-Commerce Analytics?

What are the key metrics and KPIs in E-Commerce Analytics?

The key metrics and KPIs in e-commerce analytics are specific, quantifiable measures that help you monitor the “well-being” of an online business.

•   Conversion rate: The proportion of website visitors who decide to buy something. In other words, this metric reflects how your site is converting those who are merely browsing into buyers.

•   Average order value (AOV): The mean value of products or services purchased in one transaction. Furthermore, a higher AOV may have an impact on the rate of revenue increase compared to traffic.

•   Customer Lifetime Value (CLV): The estimate of total revenue inflow that will come from one customer during the whole partnership with the business.

•   Cart Abandonment Rate: Is the metric that tracks how many customers begin the checkout process by adding items to their cart but do not complete the transaction. Metrics in this area suggest a connection between the shop’s checkout process and its level of complexity.

•   Return on Ad Spend (ROAS): The amount of money generated from every dollar that is used for advertising. This metric directly measures the profitability of your marketing campaigns.

Deep Dive:
Don’t just track these metrics in isolation. Analyze the relation between them. While a high AOV may be positive, a simultaneous high cart abandonment rate may indicate factors in pricing or shipping that deserve consideration.

How do businesses collect data for E-Commerce Analytics?

Businesses gather data for e-commerce analytics by integrating a specialized analytics platform with their online store. This method may be associated with the tools’ ability to collect user behavior data, such as clicks and conversions (it could relate to data flow that is less complex with reduced manual effort). Google Analytics, a popular tool available without cost, is often integrated with e-commerce platforms such as Shopify or WooCommerce. Besides that, you may get other types of data from your CRM (Customer Relationship Management) system, marketing platforms like Facebook Ads, or even from tools that make visual heatmaps of the activity of users on your website.

Pro Tip:
Utilize a Tag Management System (TMS) such as Google Tag Manager. This provides a means of handling website tracking codes and tags, which may relate to data collection accuracy and efficiency, and could alter developer workload.

What tools are used for E-Commerce Analytics?

All-in-one platforms:

•   Google Analytics 4 (GA4) is a freely available tool for website and app data analysis.

•   Adobe analytics: A solution that includes a greater degree of customization, often used in enterprise settings.

•   E-commerce platform analytics: Dashboards in platforms such as Shopify and BigCommerce offer a basic introduction.

Business intelligence (BI) tools:

•   Tableau and Microsoft Power BI: Used for advanced data visualization and connecting to multiple data sources.

Specialized tools:

•   Hotjar: Generates heatmaps and session recordings that help users visually grasp user behavior.

•   Ahrefs or semrush: Primarily used for SEO, they also provide data related to competitive organic traffic.

•   Klaviyo: An email marketing platform that provides customer segmentation analytics and campaign performance tracking.

How does E-Commerce Analytics help optimize the customer journey?

E-commerce analytics can impact the customer journey by presenting data-driven insights during various stages, from discovery to retention. This process is crucial as it enables identification of friction points for resolution, influencing conversion rates and customer loyalty.

Pro Tip:
Use funnel analysis to pinpoint the exact stage where customers are dropping off. If there’s a notable drop-off between the “Add to Cart” and “Checkout” stages, examining elements on that page, like shipping costs and form clarity, could be considered.

What is the difference between Descriptive, Predictive, and Prescriptive Analytics in E-Commerce?

•   Descriptive analytics: Focuses on past occurrences. It is the base of analytics. It involves the use of past data to provide standard reports, such as “How many sales were made last month?“

•   Predictive analytics: “What will happen?“ It employs statistical models and machine learning techniques to anticipate future trends. For example, “How much revenue are we likely to generate next quarter?“

•   Prescriptive analytics: This can be considered a complex variant. It utilizes data to direct to one particular action, for example, “Which customers should we target with a 15% discount to maximize our profit?“

Deep Dive:
Using all three does not necessarily require expertise in data science. Modern analytics tools often feature predictive capabilities (e.g., sales forecasting) and prescriptive advice (e.g., automated A/B testing suggestions) integrated into their dashboards, which may affect the accessibility of advanced analytics.

How is a Cohort Analysis used to understand customer behavior over time?

A cohort analysis is a method of analyzing customer behavior that tracks customers over a period of time and groups those who share a characteristic, the most common being the sign-up or first purchase date. This technique may be beneficial for observing the behavior of a specific group, allowing for the examination of potential impacts from marketing or product adjustments on customer retention and value. Comparing different cohorts enables you to assess the long-term implications of your business decisions.

Conclusion

E-commerce analytics provides metrics that may be used for customer understanding and business planning. The examination of specific metrics and the employment of appropriate tools enable the conversion of raw data into information that can be used for decision-making processes and impact long-term prospects.

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