Frequently Bought Together

Frequently Bought Together shows the user a list of ideal accompaniments or complementary products and accessories for the product they're currently looking at. Instead of needing to curate these recommendations manually or use hard-coded rules, with Miso you can dynamically personalize these suggestions in real time.

Example for Frequently Bought Together

Introduction

Frequently Bought Together is a product recommendation model that is commonly used by eCommerce and marketplace sites to help users find products that go along with the item they're looking at. It's usually presented as a ribbon on the product details page (PDP), often in addition to a You May Also Like recommendation. Frequently Bought Together can even highlight products that aren't in the same category as the current product and therefore complement it. This is like a personalized, algorithmic version of the "Complete This Look" recommendations that merchandising teams often put together manually.

Miso supports Frequently Bought Together recommendations through the special buy_together parameter on the Product to Products API. When this parameter is not set, the API will tend to suggest alternatives to the current product (as with You May Also Like). Using buy_together tells Miso to return suggestions that are likely to be purchased in addition to the current product, based on user behavior on your site and Miso's semantic understanding of the contents of your products. To drive conversions, the suggested products will also be personalized to the user's interests.

Frequently Bought Together is closely related to the Cart Cross-Sells recipe.

Before You Start

API Call / JSON Response

To fetch Frequently Bought Together recommendations, POST to the Product to Products API with the following parameters:

  • user_id: The user or anonymous visitor to personalize the recommendations for
  • product_id: The anchor product
  • buy_together: Set this parameter to true, which tells Miso to return products that are frequently bought together

The API will return a list of recommended products.

Don't forget: there are many parameters you can use to optimize and filter the results that Miso returns, such as fq , boost_fq , and diversification .
Read more in the deep dive

Example API Call

POST /v1/recommendation/product_to_products?api_key=<API_KEY> HTTP/1.1
{
    "user_id": "user-123",
    "buy_together": true,
    "product_id": "women-r1-fleece"
}

Example JSON Response

{
    "message": "success",
    "data": {
        "took": 52,
        "miso_id": "a4606bf0-ddd3-11eb-ba80-0242ac130004",
        "products": [
            {
                "product_id": "women-nano-puff",
                "title": "Women's Nano Puff Jackets",
                "price": 149,
                ..., // Additional scoring information
            },
            {
                "product_id": "women-ahnya-pants",
                "title": "Women's Ahnya Fleece Pants",
                "price": 79,
                ..., // Additional scoring information
            },
            ...
        ]
    }
}

Read more about the scoring information that Miso returns in the API response.

Before You Launch

Using this API recipe as a starting point, test API calls to your engine in development
If you haven't already, build pipelines to stream your real-time data updates to Miso via API. Your Miso solutions engineer is here to help with the integration.
Start using this API recipe to power the UX on your site in staging. If you need UX recommendations, ask your Miso solutions engineer about Bento .
Run an A/B test with Miso on simulated website traffic. If desired, your Miso solutions engineer can perform this test and provide a report.
If this is your first time launching with Miso, ask your Miso solutions engineer to unlock your production environment.
Replicate your Data and Engine API calls on production and set up the same engine in your production environment in Dojo.
Review your data and engine performance in Dojo.
Turn on this recipe for a percentage of your users and scale up to a full rollout. Notify your Miso solutions engineer who will monitor the launch metrics.

API Reference

💻

Need more info on the API? Check out our dedicated API page with all the info you could ever want.

Read API Reference
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