Overview

Average Abandonment Order Value on website is the average value of products left unpurchased in online shoppers’ carts.

What is Average Abandonment Order Value on Website?

The Average Abandonment Order Value on Website is a crucial e-commerce metric that quantifies the average worth of items left behind in shopping carts on an e-commerce website. This metric doesn’t include items bought and paid for; only the abandoned products are considered. A high AAOV provides insights into potential lost revenue and can signify various issues, from high shipping costs, complex checkout procedures or ineffective retargeting strategies.

Formula

Average Abandonment Order Value on Website AAOV = Total Value of Abandoned Carts / Number of Abandoned Carts

Example

  • Let’s say over a month, an e-commerce site has 1000 abandoned orders with a combined value of $100,000. The AAOV would be:
  • $100,000 / 1000 = $100.
  • So, on average, each abandoned shopping cart is worth $100.

Why is Average Abandonment Order Value on Website important?

  • It provides an estimate of potential lost revenue and gives a clear picture of how much you stand to gain by addressing shopping cart abandonment.
  • It offers insights into your customers’ shopping behavior and preferences.
  • Pinpoint conversion roadblocks and rectify them to enhance the user journey for greater profitability.

Which factors impact Average Abandonment Order Value on Website?

Several factors can increase your Average Abandonment Order Value on Website AAOV including high shipping costs, lack of payment options, mandatory account creation before checkout, and website navigation difficulties.

How can Average Abandonment Order Value on Website be improved?

  • Streamlining the checkout process: A lengthy or complex checkout can deter customers.
  • Being upfront about all costs: Hidden fees, such as delivery charges, often lead to cart abandonment.
  • Implementing a robust retargeting strategy: Inform your customers about their abandoned carts through personalized emails or reminder advertisements.

What is Average Abandonment Order Value on Website’s relationship with other metrics?

There is a direct correlation between Average Abandonment Order Value on Website AAOV and other e-commerce metrics like conversion rates, customer lifetime value, and shopping cart abandonment rate. Reduced AAOV often results in a higher conversion rate and customer lifetime value. Also, a high shopping cart abandonment rate tends to coincide with a high AAOV.

Free essential resources for success

  • Made to Measure Seasonal Marketing With Data-driven Success

    Made to Measure: Seasonal Marketing With Data-driven Success

    Build smarter seasonal strategies by connecting data insights directly to execution and performance.

  • Your Guide to Modern Measurement thumbnail

    Your Guide to Modern Measurement – the Causal Revolution

    Measure true marketing impact with incrementality, MMM, and causal analytics in a privacy-first world

  • The Measurement Program outer cover

    The Measurement Program

    Build a marketing measurement program with the structure, governance, and accountability needed to drive confident decisions.

Discover more from Lifesight

  • MMM vs attribution

    Published on: June 25, 2026

    MMM vs. Attribution: The 2026 Decision Checklist

    When platform ROAS stops matching real revenue, it’s time to rethink attribution and use MMM to uncover what truly drives incremental growth.

  • Aligned Measurement Framework

    Published on: June 24, 2026

    The Next Challenge in Measurement Isn’t Better Models. It’s Better Standards.

    Shared standards can turn fragmented measurement into confident decision-making across the marketing ecosystem.

  • Why MTA Is Broken – And Why Unified Measurement Is the Only Way Forward

    Published on: June 15, 2026

    Why MTA Is Broken – And Why Unified Measurement Is the Only Way Forward

    MTA can show what happened before a conversion, but not what actually caused it. Learn why modern marketers are moving toward causal measurement, incrementality, and unified measurement.