Average Order Value (AOV)¶

Definition¶

Average Order Value (AOV) refers to the average amount of money spent each time a customer places an order. It’s a key metric used to track customer purchasing behavior and assess the effectiveness of sales and marketing efforts.

Description¶

Average Order Value (AOV) is a core metric for revenue optimization and purchasing behavior analysis, measuring the average amount spent per transaction. It’s a key lever for boosting revenue without acquiring more customers.

The relevance and interpretation of this metric shift depending on the model or product:

In eCommerce and retail, AOV tracks the effectiveness of upselling and bundling
In DTC models, it reflects customer purchase behavior and product mix
In subscription add-ons or marketplaces, it helps refine cross-sell and premium strategies

A high AOV often points to value perception, strategic bundling, or customer trust. A low AOV may signal discount-driven behavior or poor merchandising. Segment by channel, product category, or customer cohort to tailor campaigns and pricing strategies.

Average Order Value (AOV) informs:

Strategic decisions, like packaging premium tiers or setting free shipping thresholds
Tactical actions, such as A/B testing cart recommendations or upsell prompts
Operational improvements, including checkout optimization or bundling logic
Cross-functional alignment, by syncing product, marketing, and sales teams on increasing revenue per transaction

Key Drivers¶

These are the main factors that directly impact the metric. Understanding these lets you know what levers you can pull to improve the outcome

Bundling and Upsell Strategy: Offering add-ons, cross-sells, or bundles at checkout can lift AOV. Without them, users stick to minimum value transactions.
Pricing Psychology and Anchoring: Strategic price placement (e.g., higher-tier default selection) can nudge users toward larger purchases.
Checkout Experience Design: A cluttered or rushed checkout process can prevent upsell discovery. AOV increases when add-ons are visible, simple, and contextual.

Improvement Tactics & Quick Wins¶

Actionable ideas to optimize this KPI, from fast, low-effort wins to strategic initiatives that drive measurable impact.

If AOV is flat, introduce cross-sell modules during checkout (e.g., “People also bought…” or “Add this for $X more”).
Add bundles or pre-set kits based on common purchase combos, especially for new or overwhelmed buyers.
Run a test shifting the default plan or product shown to the mid-tier, then track changes in AOV.
Refine checkout UX to include optional value-adds with simple toggles or tooltips to explain their benefit.
Partner with growth or revenue ops to analyze high-AOV segments, then replicate those flows and offers.

Required Datapoints to calculate the metric
- Total Revenue: The total amount of money generated from sales during the time period.
- Total Number of Orders: The total number of purchases made during the same period.
Example to show how the metric is derived

An online fashion retailer tracks AOV for Q1:
- Total Revenue: $500,000
- Number of Orders: 10,000
- AOV = $500,000 / 10,000 = $50 per order

Formula¶

Formula

\[ \mathrm{Average\ Order\ Value} = \frac{\mathrm{Total\ Revenue}}{\mathrm{Total\ Number\ of\ Orders}} \]

Data Model Definition¶

How this KPI is structured in Cube.js, including its key measures, dimensions, and calculation logic for consistent reporting.

Orders

cube(`Orders`, {
  sql: `SELECT * FROM orders`,

  measures: {
    totalRevenue: {
      sql: `total_revenue`,
      type: 'sum',
      title: 'Total Revenue',
      description: 'The total amount of money generated from sales during the time period.'
    },
    totalNumberOfOrders: {
      sql: `order_id`,
      type: 'countDistinct',
      title: 'Total Number of Orders',
      description: 'The total number of purchases made during the same period.'
    },
    averageOrderValue: {
      sql: `${totalRevenue} / NULLIF(${totalNumberOfOrders}, 0)` ,
      type: 'number',
      title: 'Average Order Value',
      description: 'Average amount of money spent each time a customer places an order.'
    }
  },

  dimensions: {
    orderId: {
      sql: `order_id`,
      type: 'string',
      primaryKey: true,
      title: 'Order ID',
      description: 'Unique identifier for each order.'
    },
    orderDate: {
      sql: `order_date`,
      type: 'time',
      title: 'Order Date',
      description: 'The date when the order was placed.'
    }
  }
})

Note: This is a reference implementation and should be used as a starting point. You’ll need to adapt it to match your own data model and schema

Positive & Negative Influences¶

Negative influences

Factors that drive the metric in an undesirable direction, often signaling risk or decline.
- Lack of Bundling and Upsell Strategy: Without effective bundling and upsell strategies, customers are more likely to make minimum value transactions, negatively impacting the Average Order Value.
- Poor Pricing Strategy: Ineffective pricing strategies that do not leverage psychological principles can result in lower Average Order Value as customers may opt for cheaper options.
- Complicated Checkout Process: A cluttered or confusing checkout process can deter customers from discovering and purchasing additional items, reducing the Average Order Value.
- Limited Payment Options: Restricting payment options can lead to cart abandonment or smaller purchases, negatively affecting the Average Order Value.
- Lack of Customer Engagement: Failure to engage customers with relevant offers or promotions can result in lower Average Order Value as customers may not be motivated to increase their purchase size.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Bundling and Upsell Strategy: Implementing effective bundling and upsell strategies encourages customers to add more items to their cart, thereby increasing the Average Order Value.
- Pricing Psychology and Anchoring: Using strategic pricing techniques, such as setting higher-tier options as defaults, can lead customers to make larger purchases, thus boosting the Average Order Value.
- Checkout Experience Design: A well-designed checkout process that highlights add-ons and cross-sells in a clear and contextual manner can lead to higher Average Order Value.
- Loyalty Programs: Offering loyalty rewards or points for higher spending can incentivize customers to increase their order size, positively impacting the Average Order Value.
- Personalized Recommendations: Providing personalized product recommendations based on customer behavior can encourage additional purchases, thereby increasing the Average Order Value.

Involved Roles & Activities¶

Involved Roles

These roles are typically responsible for implementing or monitoring this KPI:

Marketing
Product Management (PM)
Product Marketing (PMM)
Sales Manager
Activities

Common initiatives or actions associated with this KPI:

Upsell Strategy
Checkout Optimization
Bundle Testing

Funnel Stage & Type¶

AAARRR Funnel Stage
Type

This KPI is classified as a Lagging Indicator. It reflects the results of past actions or behaviors and is used to validate performance or assess the impact of previous strategies.

Supporting Leading & Lagging Metrics¶

Leading

These leading indicators influence this KPI and act as early signals that forecast future changes in this KPI.
- Product Qualified Leads: Product Qualified Leads (PQLs) indicate high-intent prospects who are more likely to make larger purchases. An increase in PQLs often forecasts an increase in Average Order Value (AOV) since these users have already demonstrated strong product engagement and are more likely to convert at higher price points.
- Activation Rate: A higher Activation Rate signals that more users are reaching meaningful product milestones, increasing their likelihood of purchasing and spending more per transaction, thus positively influencing future AOV.
- Deal Velocity: Faster Deal Velocity suggests sales cycles are shortening, which can correlate with higher deal quality and urgency, often resulting in larger order sizes and boosting average order value.
- Upsell Conversion Rates: Higher Upsell Conversion Rates reflect a greater success in moving customers to higher-tier plans or add-ons, directly driving up the average amount spent per order.
- Cross-Sell Conversion Rate: Increased Cross-Sell Conversion Rates mean more customers are adding complementary products or services to their purchases, raising the overall average order value.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Expansion Revenue Growth Rate: Growth in expansion revenue from existing customers (through upsell and cross-sell) confirms and amplifies trends seen in AOV, helping to quantify the impact of increasing order sizes on total revenue.
- Average Revenue Per User: Average Revenue Per User (ARPU) provides a broader view of revenue generation per customer, helping to contextualize changes in AOV by showing the relationship between order size and overall account monetization.
- Repeat Purchase Rate: A high Repeat Purchase Rate can amplify the effect of AOV by showing whether high-value orders are sustained over time, reinforcing the business's overall revenue health.
- Net Revenue Retention: Net Revenue Retention (NRR) quantifies the combined impact of AOV, expansion, and retention, providing confirmation of whether increasing order values are translating into lasting revenue growth.
- Conversion Rate: Conversion Rate changes can help explain shifts in AOV—if more users are converting at higher price points, this confirms that sales and marketing efforts are driving not just more purchases, but larger ones.