Average Order Value Analytics: How to Use Product Data to Increase AOV

Your Shopify analytics says AOV is €62. Good to know. But what does that actually tell you about what to do next?

Not much. Because €62 is an average of hundreds of combinations: a customer who bought one €62 item, three customers who bought €19 items with €5 add-ons, one basket with six things totaling €190. Averaged together, you get a number that describes no real customer, no real product, and no real decision.

The real question isn’t “what is my AOV?” It’s “which products, when included in an order, reliably produce a higher cart value, and which ones tend to close the transaction early with a small basket?”

That’s a product-level question. And most stores never answer it.

Why Your Overall AOV Tells You the Wrong Thing

AOV optimization advice falls into two camps. The first is price-based: raise product prices, reduce discounts, charge for shipping above a certain threshold. The second is tactical: add upsells, push bundles, trigger a post-add-to-cart popup.

Both are incomplete without product data.

Raising prices on low-AOV products might work. Or it might just drive higher cart abandonment. Upsells might increase AOV, or they might add friction that reduces conversion rate. You don’t know unless you can see what’s actually happening at the product level.

The core issue is that session-level AOV metrics (what GA4 shows, what Shopify Analytics shows) collapse all product behavior into a single number. They can’t tell you that product A is a basket builder that reliably pulls order value up, while product B tends to close transactions early with a single-item purchase. That distinction exists in your data. It’s just not surfaced.

Among the eCommerce KPIs that actually drive revenue decisions, AOV is one of the most commonly tracked but least acted-on metrics, specifically because it’s measured at the wrong level.

The Two Ways Products Move AOV

Not all products contribute to AOV the same way. At the product level, items fall into roughly three buckets.

Basket builders: These products appear disproportionately often in multi-item orders. They’re not necessarily your best-sellers or your most expensive items, but something about them triggers “while I’m at it” additions from customers. A €14 cleaning kit that shows up in 52% of orders containing three or more items is doing more AOV work than a €79 product that’s always bought solo.

Solo closers: These products tend to be the only item in the cart. Customers come looking for them specifically, buy them, and leave. High conversion rate, low AOV contribution. Valuable for traffic and acquisition, not for basket size.

True anchors: These are products whose presence in a cart correlates with a significantly higher total order value. Not because they’re expensive, but because they pull purchasing intent upward. A €38 leather wallet included in a cart shifts the average order to €104 in one store’s data. Customers who buy it tend to be in a “quality purchase” mindset, not a quick-add mindset.

Understanding which of your products fall into which bucket is the foundation of a product-level AOV strategy.

Finding Basket Composition in Stormly

Stormly’s basket composition analysis shows exactly this, at the SKU and category level, without needing to export to a spreadsheet or build a custom BI query.

Open the product co-occurrence report. Filter to the last 90 days of orders. Sort by “average cart value when product is included.” What you’re looking at is a ranked list of products by their AOV lift contribution.

In a real example from a 500-SKU home goods store: the top basket builder was a €14 accessory item that appeared in 38% of all multi-item orders. It had low individual revenue; it wouldn’t show up on a revenue dashboard. But its presence in a cart correlated with a 34% higher average order value compared to orders that didn’t include it.

The bottom of the same list showed a €24 product with strong sales volume but a 71% solo purchase rate. That product was being pushed heavily via paid ads as a primary acquisition driver. Shifting it out of the main cross-sell position and replacing it with the basket builder increased email-driven AOV from €61 to €88 over one quarter.

If you’ve been analyzing which products actually drive customer loyalty, you’ll notice some overlap: the products that build baskets and the products that drive repeat purchases aren’t always the same. Treating them as interchangeable leads to conflated strategy and underperforming campaigns.

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Which Products to Bundle, Cross-Sell, and Feature at Checkout

Once you have the co-occurrence data, the tactical decisions become straightforward.

Product pairs with high co-occurrence: If product A and product B appear together in 44% of orders, that pair is a natural bundle. You don’t need to guess: the data shows customers are already buying them together. Making that pair explicit (bundle pricing, “frequently bought together” placement) usually increases both conversion rate and AOV by reducing friction for behavior that’s already happening.

Basket builders in email and post-purchase flows: The €14 item that lifts cart value by 34% when included should not be buried in your catalog. It should appear as a featured add-on in cart emails, as a post-add-to-cart recommendation, or as a “customers also added” placement on high-traffic product pages. Its price makes objection low; its basket impact makes it valuable.

Solo closers as acquisition drivers: A product with a 73% solo purchase rate and an average cart of €22 can be a decent acquisition product if your margins support it. But don’t rely on it as your main upsell vehicle. Its buyers are not in “build a basket” mode. Separating its email segment from basket-building flows usually improves performance for both goals.

The cart abandonment report at the product level is worth checking alongside this data. A basket builder that also shows up frequently in abandoned carts may be suffering from a placement or pricing friction issue, not a demand problem.

The Products Quietly Killing Your AOV

This is the side most stores don’t check.

Look for any SKU where its inclusion in a cart correlates with a lower average order value than the store median, combined with a high solo-purchase rate. These products are not necessarily bad (they may have strong conversion rates and solid margins), but promoting them heavily as the primary discovery vehicle trains your acquisition funnel to attract low-basket buyers.

You can cross-reference this with cohort data. If the customers who bought product X as their first purchase have significantly lower 90-day LTV than cohorts who first purchased other products, that’s a signal worth acting on. The product-level view of customer lifetime value walks through how to run that analysis, and the findings often align closely with basket composition patterns.

This doesn’t mean product X is wrong. It might be a perfect retention product once customers are in the ecosystem. But centering acquisition around it is expensive if it consistently attracts low-basket, low-LTV buyers.

When Your AOV Drops: Finding the Root Cause Fast

A common situation: AOV was €68, now it’s €57. Nothing changed in pricing. Traffic looks normal.

Without product-level data, you’re guessing. With it, you can narrow the cause in a few steps:

Check which products have changed their multi-item order rate in the last 30 days vs. the previous 30
Look for basket builders that have dropped in placement or visibility (out of stock, excluded from a sale, removed from an email flow)
Check if a high-solo-purchase-rate product has been pushed more aggressively in campaigns over the same period, shifting the buyer mix toward lower-basket customers

One store found a €12 item that acted as a cart builder had gone out of stock for 11 days. Those 11 days corresponded almost exactly with a 14% AOV drop. Restocking and pushing it back into the cart cross-sell position recovered the average within three weeks. That signal doesn’t exist in a session-level dashboard.

Setting up anomaly detection on your eCommerce revenue metrics can flag AOV drops automatically rather than waiting for a weekly review to catch the problem three days late.

Three Actions to Take This Week

Product data without a plan is just more data paralysis. A concrete starting point:

Run the basket composition report in Stormly. Sort by “average cart value when product included.” Identify your top five basket builders.
Compare against your current cross-sell and email recommendations. If your top basket builder isn’t featured in those placements, swap it in.
Check the solo-close rate for your top acquisition products. If paid acquisition is driving traffic primarily to high-solo-purchase products, test a post-add-to-cart suggestion featuring a basket builder. Even a 10% attach rate on a €14 add-on across 1,000 monthly orders shifts the average meaningfully.

The goal is not to hit a specific AOV number. The goal is to build a product mix where your basket-building items are visible at the right moment in the purchase flow.

Your best-converting products analysis and your basket composition data should inform each other. High conversion rate plus strong basket-building behavior is a product worth centering your store experience around. High conversion rate plus consistently solo checkout behavior is an acquisition vehicle, not an AOV driver. Knowing the difference is what separates product decisions from guesswork.

Running this analysis quarterly is enough to catch most catalog drift. Products change their basket behavior as your assortment shifts, as you run promotions, and as your customer mix evolves. A category that drove multi-item orders 12 months ago may behave completely differently today.

See which products are driving your highest AOV orders in Stormly → Start free trial