Quickly understand changes in AOV

Diagnose daily fluctuations in Average Order Value to inform your product pricing, promotions, and merchandising strategy.

Analytics Challenge

Business Challenge

KPI Impact

Analytics Challenge

Average Order Value (AOV) is an easy formula, but the challenge is analyzing the many factors impacting the metric. With loyalty programs, promotions, changing product SKUs, discounts, different sales channels, bundles, customer cohorts, items per order, and more, quickly diagnosing which factors are actually impacting AOV takes hours of hypothesis testing.

You can easily monitor when AOV changes, but if you’re not diagnosing the critical factors driving the change, you’ll miss key signals about why that metric is changing.

AOV

AOV measures the average amount spent on every order placed with a merchant in a specific period of time. To calculate, you’d typically use:

AOV = total revenue / # of orders

Analytics Challenge

Average Order Value (AOV) is an easy formula, but the challenge is analyzing the many factors impacting the metric. With loyalty programs, promotions, changing product SKUs, discounts, different sales channels, bundles, customer cohorts, items per order, and more, quickly diagnosing which factors are actually impacting AOV takes hours of hypothesis testing.

You can easily monitor when AOV changes, but if you’re not diagnosing the critical factors driving the change, you’ll miss key signals about why that metric is changing.

Business Challenge

AOV is an all-in-one metric for keeping up with how your marketing, engagement, and pricing strategies are performing. It looks at the average amount a customer spends in each transaction in-store or online. For a SaaS or contract-based company, a related metric is Average Contract Value (ACV).

Measured alongside Customer Lifetime Value (CLV), monitoring AOV gives valuable insights into “right now” customer behavior, ensures the right return on marketing strategies, and is a signal on revenue health. When AOV changes, quickly diagnosing the driving factors behind the change gives businesses a chance to intervene and improve their overall strategies.

KPI Impact

Alongside total order volume, purchase frequency, and CLV, AOV is a key health metric for most retailers, online and off. While not a metric to look at in isolation, even small changes can have an outsized impact on total revenue and gross margin.

Here’s an example of the impact a small change (2%) can have on your top-line growth. If your e-commerce business serves 5,000 orders/week, with an AOV of $70:

    5,000 orders/week = $350K/week in revenue
    + 2% increase in AOV = $71.40
    Net new sales:
      Weekly: $7,000
      Annually: $365K - an extra week of revenue

If you can continue that increase, 2% month over month, that can add up to $2.5M of incremental revenue without increasing transaction volume.

Factors that matter when analyzing AOV

Get the most reliable answers on what’s impacting your revenue metrics by tracking AOV at the transaction or order level. Each row should represent a unique transaction, with one per transaction.

When you’re analyzing AOV, make your datasets as wide as possible. Start with the recommended fields below and add as many descriptive variables as you can to augment your analysis.

Schema Blueprint:

  • One row per transaction
  • Metric column =
    Order Total

Recommended

Order ID

Sales Channel

Product SKU(s)

Product Title / Name

Order Timestamp

Total Item Quantity

Customer City, State, Zip

List Price

Currency

Transaction Type

Tax

Sale Price

Promo Code

Discount Amount

Optional

Campaign

Loyalty Program

First Order Date

Last Order Date

Customer Age

Customer Tiering

Customer Success Tickets

Shipping Cost

Shipping Discount

Shipment Status

Fulfillment Status

Order Fulfillment Diff Days

Order Fulfillment Diff Hours

Schema Blueprint:
  • One row per transaction
  • Metric column =
    Order Total
Recommended

Order ID

Sales Channel

Product SKU(s)

Product Title / Name

Order Timestamp

Total Item Quantity

Customer City, State, Zip

List Price

Currency

Transaction Type

Tax

Sale Price

Promo Code

Discount Amount

Optional

Campaign

Loyalty Program

First Order Date

Last Order Date

Customer Age

Customer Tiering

Customer Success Tickets

Shipping Cost

Shipping Discount

Shipment Status

Fulfillment Status

Order Fulfillment Diff Days

Order Fulfillment Diff Hours

kitu-supper-coffee-mrk-white-ns

See how Kitu Super Coffee uses Sisu to diagnose AOV, faster.

Read the case study

More resources for augmenting your analysis

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Our playbook for building a cloud-native data architecture to improve your e-commerce analytics and implement augmented analytics tools.

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Designing Datasets: Four Principles to Advance Data Diagnosis

With more transactional data in cloud-native warehouses than ever before, analysts should stop aggregating their data for business intelligence tools. To help, here are four principles on designing datasets for cloud-native diagnosis.

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