✦ New — Frequently Bought Together
See which products your customers actually buy together
Two products can show up in the same order all the time and still have nothing to do with each other — they might just both be popular. Juicy's Frequently Bought Together report filters that noise out with lift, so you can tell a real bundle candidate from a coincidence.
What the report shows
For every combination of products found in your order history — pairs and triples — Juicy shows three numbers, plus a plain-language interpretation of what they mean together.
- Orders — how many orders contained the combination.
- Share of orders — that same count as a percentage of all orders in the period. It measures how much the combination matters in volume.
- Lift — how strongly the products are statistically connected, beyond both simply being popular. It's the most useful column and the least obvious one, explained below.
| Products | Orders | Share of orders | Lift | Interpretation |
|---|---|---|---|---|
| Yoga Mat + Yoga Block | 62 | 7.0% | 2.6x Strong match | Bundle candidate |
| Yoga Mat + Resistance Band Set | 43 | 4.9% | 1.3x Often together | Bundle candidate |
| Water Bottle + Gym Towel | 8 | 0.9% | 1.9x Often together | Niche pairing |
| Yoga Mat + Water Bottle | 78 | 8.8% | 0.8x As expected | Popular coincidence |
Example data for illustration. Toggle between Product pairs and Product triples to see three-way combinations too.
Understanding lift
Some products appear together in orders simply because both are popular — if half your customers buy your best-selling serum and half buy your best-selling cleanser, plenty of orders will contain both, even if no one thinks of them as a pair. Lift filters that effect out by comparing how often two products are actually bought together with how often that would happen by pure chance, given how popular each one is on its own.
Worked example
Say you run a cosmetics store and want to know whether three products are real pairs, or just coincidences. Last month you had 1,000 orders. Here's how often each product showed up on its own:
| Product | Orders with the product | Share |
|---|---|---|
| Sunscreen | 200 orders | 20% |
| After-Sun Lotion | 100 orders | 10% |
| Hairbrush | 400 orders | 40% |
If two products were totally unrelated, chance predicts how often they'd land in the same order (their two shares multiplied together, times 1,000 orders). Comparing that to what actually happened gives the lift:
| Product A | Product B | Orders with both | If unrelated | Lift |
|---|---|---|---|---|
| Sunscreen | After-Sun Lotion | 60 orders | 20 orders | 3.0x |
| Sunscreen | Hairbrush | 80 orders | 80 orders | 1.0x |
| After-Sun Lotion | Hairbrush | 30 orders | 40 orders | 0.75x |
Sunscreen + After-Sun Lotion sold together 3 times as often as chance predicts — customers are clearly buying them as a pair, a genuine bundle candidate. Sunscreen + Hairbrush appeared together the most in raw orders (80), but purely because both are bestsellers — a lift of exactly 1.0x means it's pure coincidence, not a real connection. After-Sun Lotion + Hairbrush lands below chance at 0.75x: customers who buy one tend not to buy the other.
Reading the lift bands
- 2.0x+ Strong match — customers actively combine these products, roughly twice as often as chance predicts, or more.
- 1.2x–2.0x Often together — a real, meaningful connection worth acting on.
- ~1.0x As expected — the products are probably not connected; they just both sell well individually.
- <0.8x Rarely together — customers who buy one tend to avoid the other, common with products that substitute for each other.
Reading lift and share together
Lift and share of orders answer different questions. The interesting combinations are the ones where both are worth acting on.
What to do with the results
- Build bundles. Take your strongest pairs (high lift, high share) and offer them as a set, with or without a small discount — customers have already told you these belong together.
- Improve recommendations. Show the matching product on the product page, in the cart, or in a post-purchase upsell. Strong pairs make the best "you might also like" candidates.
- Plan marketing together. Advertise strong pairs in the same campaign or feature them together in a newsletter, instead of treating every product separately.
- Check stock planning. If two products sell together, they should be in stock together — running out of one half of a strong pair can quietly cost you sales of the other half.
FAQ
What does "frequently bought together" mean in Shopify analytics?
It refers to a report that shows which products customers buy in the same order. Juicy's Frequently Bought Together report finds these combinations automatically from your order history and ranks them by how often they occur and how strongly they're statistically connected, for both product pairs and product triples.
What is lift in a frequently bought together report?
Lift measures how strongly two products are actually connected, beyond both simply being popular. It compares how often two products are bought together with how often that would happen by pure chance, given how popular each product is on its own. A lift around 1.0x means the pairing is coincidental; a lift of 2.0x means the pairing happens twice as often as chance would predict, meaning customers are deliberately buying them as a set.
What's a good lift score for creating a product bundle?
A lift of 1.2x or higher, combined with a meaningful share of orders, generally indicates a genuine bundle candidate. The strongest combinations, often labeled "Strong match", sit at 2.0x lift or above. A high lift with a low share of orders points to a real but rare connection, worth cross-selling but not a headline bundle.
How is share of orders different from lift?
Share of orders is simply what percentage of all orders in the selected period contain a given product combination — it measures volume. Lift measures whether that combination happens more than chance would predict — it measures intent. A pair can have a high share of orders just because both products are individually popular, without any real connection; lift filters that effect out.
Related: Product performance & gross profit · Stock & days of inventory
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