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Average Order Value (AOV)

Understand the Average Order Value (AOV) Concept

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Average Order Value (AOV)

In This Article
  • What is the definition of average order value (AOV)?
  • What is the formula for calculating the average order value (AOV)?
  • How do you interpret the average order value (AOV) metric?
  • How can a company increase its AOV?

Average Order Value (AOV) Formula

By measuring the average order value (AOV), a company – most often operating in the e-commerce vertical – can obtain insights regarding the spending patterns of its customers.

In particular, tracking AOV can help understand if upselling/cross-selling efforts have been paying off.

  • Upselling: Strategy to convince existing customers to upgrade to different products or plans with higher pricing (i.e. upgrade)
  • Cross-Selling: Offering complimentary (or related) products to existing customers

If so, AOV over time will increase year-over-year, which is a positive signal that the current strategy in working as planned.

Clearly, companies desire their customers to spend more in each order, as this implies their product/service offerings are complementary.

The formula for calculating the average order value (AOV) is as follows:

AOV Formula

  • Average Order Value (AOV) = Total Revenue / Number of Orders Placed

Average Order Value (AOV) Calculation Example

To illustrate, let’s assume an e-commerce company has generated $2 million in revenue last year with 100,000 total orders.

Upon dividing the company’s revenue figure by the order count, we arrive at the AOV.

  • Average Order Value (AOV) = $2 million / 100,000 = $20

Here, our company’s AOV is equal to $20 – the typical customer order size.

Increase Average Order Value (AOV)

Companies can increase their AOV by identifying and segmenting their top customers – i.e. higher % of total revenue contribution – and then delivering them personalized sales and marketing tactics.

Not only does this encourage these high-value customers to purchase more and increase their AOV, but it also helps with customer retention.

Furthermore, patterns can be recognized where the top customers share traits, which can help guide the go-to-market strategy – i.e. target more similar customers as market demand (and value-add) has been confirmed.

In addition, companies can better understand the needs of their customers and introduce new products/services to address those needs appropriately – developed either internally or via M&A.

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