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AOV

AOV

(Average Order Value)

ROAS reveals how much revenue your ads generate for every dollar spent — and whether that spend is truly working for you. Learn how to calculate it and apply it to maximize your marketing returns.

Table of Contents
Understanding AOVHow to Calculate AOVWhat’s Considered a Healthy AOV?AOV FAQ
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Understanding Average Order Value (AOV)

For ecommerce businesses aiming to understand customer purchasing behavior, Average Order Value (AOV) is a key metric. AOV measures the average dollar amount spent each time a customer places an order on your website. This metric provides insights into how much revenue you generate per transaction and helps strategize ways to increase revenue, without necessarily increasing the number of customers.

How to Calculate AOV

AOV = Total Revenue / Number of Orders

Give it a go in our AOV calculator!

AOV Calculator

Average Order Value:
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Calculation Example

Suppose your target AOV needs to be $75 or higher to meet your profitability goals. If you've had 500 orders this month, calculate the minimum revenue you need to generate:

Revenue = AOV × Number of Orders

Revenue = $75 × 500

Revenue = $37,500

Aim to keep your revenue at or above $37,500 for every 500 orders to maintain your desired AOV.

Note: AOV offers a clear view of your customers' average spending patterns but doesn't account for individual customer lifetime value or variability in customer behavior. It focuses solely on the average amount spent per order, making it valuable for assessing your overall pricing strategy and identifying opportunities to increase revenue per customer.

What Tools Measure LTV:CAC Ratio?

You’d think calculating your LTV:CAC ratio would be straightforward — but surprisingly, most tools don’t make it easy.

This metric requires connecting costs with customer value over time — something very few platforms are built to do natively. Let’s break down where you can get this data, where the gaps are, and how Incendium fills them.

GA4: Can You Measure LTV:CAC?

Not easily. GA4 doesn’t show customer acquisition cost at all, and its Lifetime Value report is:

● Hidden under the “Explore” tab.

● Limited to revenue per user over a fixed period (e.g. 90 days).

● Lacking cohort-level breakdowns.

● Not connected to ad spend or margin data.

So while you can approximate parts of LTV in GA4, you’ll need spreadsheets to:

● Pull in ad costs from Google Ads, Meta, etc.

● Match spend to customer cohorts.

● Adjust for churn, returns, or contribution margin.

It’s doable — but; manual, error-prone, and not scalable.

❌ No CAC
❌ No contribution margin
❌ No retention-based LTV
❌ No channel or campaign granularity

Spreadsheet-Based Models

Many marketers and finance teams rely on custom spreadsheet models. These usually involve:

● Exporting revenue data from Shopify or GA4.

● Pulling in ad costs from platforms like Meta or Google.

● Matching acquisition dates to cohorts.

● Calculating LTV over time.

● Segmenting CAC by channel (if possible).

This can work for small teams — but:

● It's time-consuming.

● Easy to break as your business grows.

● Difficult to segment and troubleshoot.

● Hard to update frequently enough to make real-time decisions.

✅ Flexible
❌ Time-consuming
❌ Not real-time
❌ High error risk

Incendium: LTV:CAC Done Right

Incendium was built for this. It automatically connects:

● Ad platform spend (Google, Meta, TikTok, etc).

● Revenue and margins (from Shopify, Stripe, etc).

● Customer retention curves.

● Channel and campaign attribution.

● Cohort-based performance over time.

Then it presents your true LTV:CAC ratios — by channel, campaign, customer segment, and acquisition cohort — without the need for spreadsheets.

Whether you want to:

● See if paid search is actually profitable.

● Compare returning vs new customers.

● Measure payback periods.

● Or optimize toward high-LTV audiences.

…you’ll have it, instantly.

✅ Automated
✅ Channel + cohort breakdowns
✅ Margin-based LTV
✅ Attribution-integrated
✅ Real-time, self-updating

What’s Considered a Healthy AOV?

A "healthy" AOV depends heavily on your industry, product pricing, and profit margins. While benchmarks vary widely, the right target for your business should support profitable unit economics:

● High-margin products (furniture, electronics, jewelry) naturally have higher AOV ($200-$500+) but may require longer sales cycles and higher customer acquisition costs.

● Low-margin businesses (consumables, accessories, digital products) typically see lower AOV ($30-$80) but can achieve profitability through volume, repeat purchases, and lower fulfillment costs.

● Subscription or replenishment models may accept lower initial AOV ($25-$50) if customer lifetime value and retention rates justify the economics over multiple purchases.

Tip: The key is ensuring your AOV provides sufficient gross profit to cover customer acquisition costs, operational expenses, and desired margins—not chasing arbitrary industry benchmarks.

AOV FAQ

How can I increase my AOV without discounting?

Should AOV be measured differently for new vs. returning customers?

Does a high AOV always mean better profitability?

How does AOV relate to customer acquisition cost (CAC)?

Should I track AOV by traffic source or marketing channel?

Is there a risk in focusing too heavily on increasing AOV?

What Tools Measure LTV:CAC Ratio?

You’d think calculating your LTV:CAC ratio would be straightforward — but surprisingly, most tools don’t make it easy.

This metric requires connecting costs with customer value over time — something very few platforms are built to do natively. Let’s break down where you can get this data, where the gaps are, and how Incendium fills them.

GA4: Can You Measure LTV:CAC?

Not easily. GA4 doesn’t show customer acquisition cost at all, and its Lifetime Value report is:

● Hidden under the “Explore” tab.

● Limited to revenue per user over a fixed period (e.g. 90 days).

● Lacking cohort-level breakdowns.

● Not connected to ad spend or margin data.

So while you can approximate parts of LTV in GA4, you’ll need spreadsheets to:

● Pull in ad costs from Google Ads, Meta, etc.

● Match spend to customer cohorts.

● Adjust for churn, returns, or contribution margin.

It’s doable — but; manual, error-prone, and not scalable.

❌ No CAC
❌ No contribution margin
❌ No retention-based LTV
❌ No channel or campaign granularity

Spreadsheet-Based Models

Many marketers and finance teams rely on custom spreadsheet models. These usually involve:

● Exporting revenue data from Shopify or GA4.

● Pulling in ad costs from platforms like Meta or Google.

● Matching acquisition dates to cohorts.

● Calculating LTV over time.

● Segmenting CAC by channel (if possible).

This can work for small teams — but:

● It's time-consuming.

● Easy to break as your business grows.

● Difficult to segment and troubleshoot.

● Hard to update frequently enough to make real-time decisions.

✅ Flexible
❌ Time-consuming
❌ Not real-time
❌ High error risk

Incendium: LTV:CAC Done Right

Incendium was built for this. It automatically connects:

● Ad platform spend (Google, Meta, TikTok, etc).

● Revenue and margins (from Shopify, Stripe, etc).

● Customer retention curves.

● Channel and campaign attribution.

● Cohort-based performance over time.

Then it presents your true LTV:CAC ratios — by channel, campaign, customer segment, and acquisition cohort — without the need for spreadsheets.

Whether you want to:

● See if paid search is actually profitable.

● Compare returning vs new customers.

● Measure payback periods.

● Or optimize toward high-LTV audiences.

…you’ll have it, instantly.

✅ Automated
✅ Channel + cohort breakdowns
✅ Margin-based LTV
✅ Attribution-integrated
✅ Real-time, self-updating

What Tools Measure LTV:CAC Ratio?

You’d think calculating your LTV:CAC ratio would be straightforward — but surprisingly, most tools don’t make it easy.

This metric requires connecting costs with customer value over time — something very few platforms are built to do natively. Let’s break down where you can get this data, where the gaps are, and how Incendium fills them.

GA4: Can You Measure LTV:CAC?

Not easily. GA4 doesn’t show customer acquisition cost at all, and its Lifetime Value report is:

● Hidden under the “Explore” tab.

● Limited to revenue per user over a fixed period (e.g. 90 days).

● Lacking cohort-level breakdowns.

● Not connected to ad spend or margin data.

So while you can approximate parts of LTV in GA4, you’ll need spreadsheets to:

● Pull in ad costs from Google Ads, Meta, etc.

● Match spend to customer cohorts.

● Adjust for churn, returns, or contribution margin.

It’s doable — but; manual, error-prone, and not scalable.

❌ No CAC
❌ No contribution margin
❌ No retention-based LTV
❌ No channel or campaign granularity

Spreadsheet-Based Models

Many marketers and finance teams rely on custom spreadsheet models. These usually involve:

● Exporting revenue data from Shopify or GA4.

● Pulling in ad costs from platforms like Meta or Google.

● Matching acquisition dates to cohorts.

● Calculating LTV over time.

● Segmenting CAC by channel (if possible).

This can work for small teams — but:

● It's time-consuming.

● Easy to break as your business grows.

● Difficult to segment and troubleshoot.

● Hard to update frequently enough to make real-time decisions.

✅ Flexible
❌ Time-consuming
❌ Not real-time
❌ High error risk

Incendium: LTV:CAC Done Right

Incendium was built for this. It automatically connects:

● Ad platform spend (Google, Meta, TikTok, etc).

● Revenue and margins (from Shopify, Stripe, etc).

● Customer retention curves.

● Channel and campaign attribution.

● Cohort-based performance over time.

Then it presents your true LTV:CAC ratios — by channel, campaign, customer segment, and acquisition cohort — without the need for spreadsheets.

Whether you want to:

● See if paid search is actually profitable.

● Compare returning vs new customers.

● Measure payback periods.

● Or optimize toward high-LTV audiences.

…you’ll have it, instantly.

✅ Automated
✅ Channel + cohort breakdowns
✅ Margin-based LTV
✅ Attribution-integrated
✅ Real-time, self-updating

AOV Related Reading

3 Common AOV Pitfalls and How to Avoid Them
AOV
3 Common AOV Pitfalls and How to Avoid Them

AOV is valuable for understanding transaction patterns, but focusing on it in isolation can lead to short-sighted strategies and customer experience issues.