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# Understanding the Frequently bought together report

The Frequently bought together report shows you which of your products customers buy in the same order. You find it under Products, in the "Frequently bought together" tab.

It answers a simple question: if someone puts product A in their cart, what else do they usually buy with it?

## What you see

For every combination you see:

* **Orders**: in how many orders the combination appeared. If "Yoga mat + Yoga block" shows 100, then 100 orders contained both products.
* **Share of orders**: the same number as a percentage of all orders in the selected period. 100 orders out of 4,000 total is a share of 2.5%.
* **Lift**: how strongly the products belong together. More on this below, because it is the most useful column and the least obvious one.

You can change the date range at the top and sort the table by any column.

## What lift means

Some products appear together in orders just because both are popular. If half your customers buy your bestselling serum and half buy your bestselling cleanser, plenty of orders will contain both, even if no customer ever thinks of them as a set.

Lift filters this effect out. It compares how often two products are actually bought together with how often that would happen by pure chance, given how popular each product is on its own.

* **Lift around 1.0x** means the combination happens about as often  as chance predicts. The products are probably not connected. They just sell well individually.
* **Lift clearly above 1.0x** means customers actively combine these products. A lift of 2.0x means the combination appears twice as often as chance would predict,
3.0x three times as often.
* **Lift below 1.0x** means customers who buy one product tend to avoid the other. You see this with products that replace each other, like two sizes of the same
starter kit.

The report shows a label next to each lift value so you can scan the table quickly: "Strong match" (2.0x and above), "Often together" (1.2x and above), "As expected"(around 1.0x), and "Rarely together" (below 0.8x).

### Example for lift

| Read here for an example of lift

Lets say you are a cosmetic shop. And you are interested to find out, if the following products are a bundle or not.
In the last month you had **1,000 orders in your store**. You count the **number of orders** where you find each of the products.

| Product | Orders with the product | Share |
| ---- |
| Sunscreen | 200 orders | 20% |
| After-Sun Lotion | 100 orders | 10% |
| Hairbrush | 400 orders | 40% |

If the **Sunscreen** and the **After-Sun Lotion** were totally **unrelated**, we would expect them to be together in about **20 orders** (20% x 10% x 1,000 orders).
If the **Sunscreen** and the **Hairbrush** were totally **unrelated**, we would expect them to be together in about **80 orders** (20% x 40% x 1,000 orders).
If the **After-Sun Lotion** and the **Hairbrush** were totally **unrelated**, we would expect them to be together in about **40 orders** (10% x 40% x 1,000 orders).

Now you count how often the products were actually sold together:
| Product A | Product B | Orders with both products | If unrelated | Lift |
| ---- |
| Sunscreen | After-Sun Lotion | 60 orders | 20 orders | 60 / 20 = 3.0 |
| Sunscreen | Hairbrush | 80 orders | 80 orders | 80 / 80 = 1.0 |
| After-Sun Lotion | Hairbrush | 30 orders | 40 orders | 30 / 40 = 0.75 |

The numbers reveal a lift of **3.0** for the **Sunscreen** and the **After-Sun Lotion**. That means they were sold together 3 times as often as chance would predict.
The **Sunscreen** and the **Hairbrush** were sold together most often (80 orders), but only because both are high sellers. A lift of **1.0** means exactly what chance predicts.
The **After-Sun Lotion** and the **Hairbrush** even land below chance at **0.75**: customers who buy one tend not to buy the other.

## Reading the two numbers together

Lift and share of orders answer different questions, and the interesting combinations are the ones where both are good.

| Lift | Share | Interpretation |
| ---- |
| High 💚 | High 💚 | Your best **bundle candidates**. Customers combine these on purpose, and it happens often enough to matter for revenue. |
| High 💚 | Low ⬇️ | A real connection, but rare. Often a niche pairing. It can still be worth showing one product on the other's product page, but do not expect a bundle to move big numbers. |
| Low ⬇️ | High 💚 | Two bestsellers overlapping by coincidence. Don't do bundle discounts, mostly gives margin away on orders you would have received anyway. |


## What to do with the results

A few practical ways stores use this report:

1. **Build bundles.** Take your strongest pairs and offer them as a set, with or without a small discount. Customers have already told you these belong together.
2. **Improve product recommendations.** Show the matching product on the product page, in the cart, or in the post-purchase upsell. Strong pairs make the best "You might also like" candidates.
3. **Plan marketing together.** Advertise strong pairs in the same campaign or feature them together in a newsletter, instead of treating every product separately.
4. **Check your stock planning.** If two products sell together, they should be in stock together. Running out of one half of a strong pair can cost you sales of the other half.

## Good to know

* A combination only appears in the report after it occurred in at least 3 orders in the selected period. This keeps one-off coincidences out.
* Each table shows the top 20 combinations, ranked by the number of orders.
* The percentages are based on all orders in the selected date range, including orders with a single product.
* **If your store is small or the date range is short, expect few results and take lift values with a grain of salt. Ten orders are not enough to prove a pattern. A longer date range gives more reliable numbers.**
* Combinations are counted per order, not per customer. Someone buying product A today and product B next week does not count as a pair.