What is eCommerce Analytics?

If you are in the business of selling online, you are up against 7.9 million eCommerce retailers worldwide (and growing as you read this). You are also dealing with a mountain of complex data related to online buyers. 

To stay ahead of the competition, online sellers need an eCommerce analytics solution. eCommerce analytics is the process of gathering and monitoring data related to the entire online buyer journey, from acquisition to checkout. 

The Role of Analytics in eCommerce

Data analytics are vital in every aspect of running and growing an eCommerce business. When you drill down into the metrics and KPIs, you discover which activities drive traffic, increase engagement, and generate more sales.  

For example, correlating various engagement metrics helps you identify ways to improve customer experience. In an increasingly competitive eCommerce environment, user experience can make the difference between buying or leaving. 

Why Making Sense of eCommerce KPIs Is a Challenge

With the overwhelming amount of data, online retailers can spend more time managing their data than their shop. Data analysis in eCommerce is also extremely time-sensitive and requires cross-silo correlation. 

Another challenge is constantly changing market behavior that causes many false positives or negatives, leading to alert storms. This puts analysts back at square one: too much data to evaluate, contextualize and classify, not to mention the need to configure thresholds manually.    

Which eCommerce Metrics and Dimensions Should You Measure?

To get actionable insights, eCommerce companies need to focus their analytics efforts on KPIs that provide visibility into the customer journey. We’ve compiled a list of the best metrics and dimensions to monitor and how AI simplifies and improves eCommerce analytics. 

5 Top eCommerce Metrics to Measure

1. Ad Performance 

Display ads are a major traffic driver in eCommerce. Analyzing ad performance helps you Identify which ads are generating the highest quality traffic and double down on their success. Ads can also get costly, so you want to monitor each campaign to prevent runaway costs. 

When you run multiple campaigns across multiple networks, an AI-driven analytics tool connects and monitors all of them. Some can also correlate between data and distill billions of data events into one single, scored alert. This reduces manual efforts to a minimum while you receive maximum insights.

2. Conversion Funnel or Purchase Funnel 

Conversion rate is one of the most looked at metrics in eCommerce. To improve conversions you need to monitor every touchpoint in the online path to purchase.

An AI-driven analytics solution autonomously learns the normal behavioral pattern of conversions throughout the purchase funnel and adapts to pattern changes. Real-time alerts are automatically sent when the conversion rate is exceptionally high or low. 

3. Cart Abandonment 

Cart abandonment is directly related to revenue, and that’s why time is critical. Any increase in cart abandonment can indicate an incident, and you need to know immediately. 

AI business monitoring solutions identify irregular data behavior based on the patterns detected by its machine learning capabilities rather than static thresholds. The solutions can also score alerts according to severity, significantly reducing the number of alerts and providing you with related metrics that point to the cause. 

4. Returning Buyers

Returning buyers are satisfied buyers, and you want their numbers to increase. Measure the ratio of new vs. returning buyers to understand the effectiveness of your acquisition and retention marketing. Whenever you make a change in your marketing efforts, watch how the metric reacts.

When buyers aren’t returning, they weren’t happy with the product or the user experience on your site. AI-driven technologies can identify anomalies in the metric’s behavior in real-time and autonomously identify related incidents that provide additional insights into the customer experience.

5. Churn

Another critical metric that should be top of mind is the churn rate. A decreasing rate means you are doing something right with your retention marketing; an increasing rate is a red flag. You need to drill into your analytics to find reasons. You also want to act quickly because losing customers means losing revenue.

An analytics platform leveraging machine learning to autonomously detect data patterns and correlate with all data from other business areas can speed up the process. 

Important Dimensions for eCommerce Businesses

1. Region

See how metrics are region-specific. Which products are popular in which regions, which marketing campaigns work better in one region than another? Drill down into the entire buyer journey per different regions and understand how the location affects user behavior. An AI-based analytics tool does it autonomously and alerts you about significant changes that require your attention.

2. Browser

As the reach of your eCommerce business grows, so does the variety of browsers that potential customers use to access your site. Make sure your site is accessible, monitor metrics per browser to identify variations, and find out why they occur.

3. Device type

The same is valid for device type. For example, if there’s a problem with payment through a specific provider only on a particular device, you will not notice a drop in payment completion. An AI-driven tool monitors all data, connects incidents and alerts you about the issue. 

AI In eCommerce Analytics for More Granularity and Focus

Let’s sum up the advantages AI provides for eCommerce analytics. 

Ai-based monitoring eliminates false alerts because it autonomously identifies data patterns and deviations from them. An alert-scoring system ensures you receive only highly relevant alerts. 

This allows you to take swift action and prevent damage and financial loss because the system reacts to real-time data rather than analyzing historical data.

You receive full coverage of all metrics across all business units, allowing for the correlation of seemingly unrelated incidents and contextualizing the data behavior.

Additionally, the AI-driven tool provides granularity in monitoring eCommerce KPIs by looking into anomalies for each permutation of each measured metric.


Written by Anodot

Anodot leads in Autonomous Business Monitoring, offering real-time incident detection and innovative cloud cost management solutions with a primary focus on partnerships and MSP collaboration. Our machine learning platform not only identifies business incidents promptly but also optimizes cloud resources, reducing waste. By reducing alert noise by up to 95 percent and slashing time to detection by as much as 80 percent, Anodot has helped customers recover millions in time and revenue.

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