Increase average order value with bundles
Bundles raise order value when they match real buying patterns. Find and test the right ones.
In short
- Bundle what shoppers view together but rarely buy together. That gap is the leak a bundle plugs.
- Recommendation clicks are ~7% of visits but ~26% of revenue; the offer only earns that when it's relevant, not random.
- Pair the hero with its add-on, not a near-twin. The best bundle removes a decision, not just dollars.
Average order value
+0%
From bundles matched to real co-purchase behavior.
Trend
Illustrative. Measured on your data first.
Most "frequently bought together" widgets are doing pattern-matching on the whole catalog, not on what your shoppers actually pair in a session, so they suggest a phone case for a kettle. The bundles that lift AOV come from the combinations buyers already reach for together. Salesforce found that visits where someone clicks a product recommendation are only 7% of traffic but drive 26% of revenue, which tells you th…
What's the problem?
You want each order to be worth more, but generic 'frequently bought together' widgets feel random and rarely move AOV. You're not sure which products genuinely belong together for your shoppers.
Why does this happen?
- Bundles are based on guesswork, not on what your shoppers actually view and buy together.
- The bundle offer appears too late, after the buy decision is already made.
- Discounts on bundles erode margin without a measured lift to justify them.
- The bundle competes with itself. Showing a 'buy all three' offer next to three individually-priced 'Add to cart' buttons gives the shopper a cheaper-feeling default (the single item) so they take it and skip the bundl…
- The pairing is logical to you, not to the buyer. You bundle the jacket with the matching pants because they're the same collection. Shoppers bundle the jacket with the beanie because that's how they picture wearing it.…
- The bundle saves money but not decisions. A good bundle removes a choice the shopper was dreading: which filter fits this machine, which cable, which size of refill. If your bundle is just 'two things at 10% off' with…
- One bundle, everywhere. A static 'complete the look' block on every PDP ignores that the right add-on for a $200 jacket is different from the right add-on for a $20 tee. Recommendations that move AOV are tuned per produ…
What does the research show?
Independent researchFigures below are from independent studies, not StorePilot data. They're why this problem is worth testing on your own store.
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Visits where a shopper clicks a product recommendation are just 7% of all visits but drive 24% of orders and 26% of revenue, across 150M+ shoppers and 250M+ visits.
Salesforce (Commerce Cloud), 'Personalized Product Recommendations Drive Just 7% of Visits but 26% of Revenue' ↗ -
35% of what consumers buy on Amazon comes from algorithm-driven product recommendations, a benchmark for how much behaviour-based pairing can move basket size.
McKinsey & Company, 'How retailers can keep up with consumers' ↗ -
Skincare brand NuFACE A/B-tested a 'free shipping over $75' threshold message and saw orders rise 90% and average order value rise 7.32% (96% confidence) from the same traffic.
VWO success story, NuFACE free-shipping threshold A/B test ↗ -
81% of shoppers say they're willing to spend more to reach a free-shipping threshold, a lever bundles can pull to nudge basket size.
FedEx / Morning Consult survey of 2,103 US consumers ↗ -
Personalization typically drives a 10–15% revenue lift, ranging 5–25% depending on sector and execution, the band a well-targeted bundle program plays in.
McKinsey & Company ↗
How does StorePilot AI fix it?
- StorePilot looks at real co-viewing and co-purchase behavior to suggest bundles that match intent.
- It generates a bundle block placed where it actually influences the decision and A/B tests it.
- Because revenue-per-visitor is the primary metric, you see whether the bundle grows total revenue, not just a vanity AOV number.
How do you fix it, step by step?
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Pull your real co-view and co-purchase pairs
Before designing any bundle, look at which products get viewed in the same session and which actually get bought together. The gap between the two (high co-view, low co-purchase) is where a bundle has room to convert intent that's currently leaking.
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Bundle the add-on, not the twin
Pair the hero product with the thing that completes the job (the beanie, the filter, the refill, the cable), not another version of the same item. The best add-on removes a decision the buyer was going to have to make anyway.
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Place it where the decision is still open
Surface the bundle on the PDP and in the cart drawer, while the shopper is still choosing, not on a post-add upsell page after they've mentally closed the purchase. The same offer converts very differently before versus after 'Add to cart.'
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Make the combined buy the path of least resistance
Show one clear 'Add the set' action with the bundle total, so the shopper isn't doing the arithmetic across three separate buttons. The bundle should feel like the easy default, not a fourth thing to evaluate.
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Price to protect margin, then justify with a threshold
Keep any bundle discount small, and where it makes sense, tie the bundle to a free-shipping threshold. 81% of shoppers will spend more to hit one, so a bundle that crosses it can pay for itself without deep discounting.
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Run it as a real test and read AOV plus conversion together
Split traffic, hold it to significance, and watch that the AOV gain doesn't quietly drop conversion. A bundle that lifts order value 8% but cuts checkout rate isn't a win, so measure both on your own store before rolling it out.
An illustrative example
Demo data- What StorePilot detects
- Shoppers who view this jacket frequently also view the matching beanie within the same session, but rarely buy both.
- The fix it builds & tests
- Add a 'Complete the look' bundle on the jacket PDP with the beanie at a small, margin-safe bundle price.
- The projected outcome
- Example projection: +8% average order value with stable conversion. (Illustrative demo figure, measured on your store first.)
Key takeaways
- Bundle what shoppers view together but rarely buy together. That gap is the leak a bundle plugs.
- Recommendation clicks are ~7% of visits but ~26% of revenue; the offer only earns that when it's relevant, not random.
- Pair the hero with its add-on, not a near-twin. The best bundle removes a decision, not just dollars.
- 81% of shoppers will spend more to hit free shipping, so tie bundles to a threshold instead of deep-discounting.
This guide is part of the StorePilot average order value playbook. If this is costing you sales, look at Use a free-shipping threshold to lift order value and Add a post-purchase upsell that shoppers welcome next.
