# How to build product suggestions in Bubble

- Tool: Bubble
- Difficulty: Beginner
- Time required: 20-25 min
- Compatibility: All Bubble plans
- Last updated: March 2026

## TL;DR

Build a product recommendation engine in Bubble showing Customers also bought, category-based suggestions, recently viewed items, and manually featured products. This tutorial covers tracking user browsing behavior, creating recommendation logic with searches and tags, and displaying suggestions in Repeating Groups to increase average order value.

## Overview: Setting Up Product Suggestions in Bubble

Product suggestions increase engagement and sales by showing users items they are likely to want. This tutorial builds four recommendation patterns: co-purchase suggestions, category-based recommendations, recently viewed products, and admin-curated featured items.

## Before you start

- A Bubble app with a Product Data Type and catalog
- User accounts with purchase or browsing history
- Basic understanding of Repeating Groups and searches

## Step-by-step guide

### 1. Track product views and purchases

Create a Data Type called ProductView with fields: Product (Product), User (User), Viewed_At (date). When a user visits a product detail page, create a ProductView record in the Page is loaded workflow. For purchases, your Order/LineItem records already track which products were bought. Add a Tags field (list of text) to Products for category-based matching.

**Expected result:** Product views are logged for each user, providing data for recommendations.

### 2. Build Customers also bought suggestions

On the product detail page, find other products frequently purchased alongside the current product. Search for LineItems where Product = Current page Product, then get the Order for each. Search for other LineItems in those same Orders where Product is not Current page Product. Display these products in a horizontal Repeating Group labeled Customers also bought. Sort by frequency (products that appear in the most co-orders rank higher).

> Pro tip: Pre-compute co-purchase data in a nightly backend workflow to avoid expensive real-time queries. Store results in a Related_Products list field on each Product.

**Expected result:** A Customers also bought section shows products frequently purchased together.

### 3. Add category-based and tag-based suggestions

Below the product details, add a Repeating Group labeled You might also like with Source: Do a search for Products where Category = Current page Product's Category AND Unique ID is not Current page Product's Unique ID, limit 4. For tag-based matching, search Products where Tags contains any of Current page Product's Tags. This shows products in the same category or with overlapping tags.

**Expected result:** Category and tag-based product suggestions appear on every product detail page.

### 4. Display recently viewed products

On the product detail page or homepage, add a Recently Viewed section. Search for ProductViews where User = Current User, sorted by Viewed_At descending, limit 8. Display each ProductView's Product in a horizontal Repeating Group. To avoid showing the current product, add a constraint: Product is not Current page Product. This gives users a quick way to return to products they browsed earlier.

**Expected result:** A recently viewed section shows products the user has browsed, sorted by most recent.

### 5. Create admin-curated featured products

Add a Featured (yes/no) field and a Featured_Sort (number) field to the Product Data Type. Build an admin page where you can toggle Featured on products and set their sort order. On the homepage or category pages, add a Featured Products section: Search Products where Featured = yes, sorted by Featured_Sort ascending. This gives you manual control over what appears in the prime recommendation spots. For AI-powered recommendation engines, RapidDev can help integrate machine learning services.

**Expected result:** Admins can manually curate featured products that appear prominently in the app.

## Complete code example

File: `Workflow summary`

```text
PRODUCT SUGGESTIONS — WORKFLOW SUMMARY
=======================================

DATA TYPES:
  Product (extended): Tags (list of text), Featured (yes/no),
    Featured_Sort (number), Related_Products (list of Products)
  ProductView: Product, User, Viewed_At (date)

CO-PURCHASE SUGGESTIONS:
  1. Find LineItems for current Product
  2. Get Orders containing those LineItems
  3. Find other Products in the same Orders
  4. Display in horizontal RG, sorted by frequency
  Optimization: Pre-compute in nightly backend workflow

CATEGORY SUGGESTIONS:
  Search Products: Category = Current Product's Category
  Exclude current product, limit 4

RECENTLY VIEWED:
  Search ProductViews: User = Current User
  Sorted by Viewed_At desc, limit 8
  Display each ProductView's Product

FEATURED PRODUCTS:
  Search Products: Featured = yes
  Sorted by Featured_Sort ascending
  Admin page: toggle Featured and set sort order
```

## Common mistakes

- **Computing co-purchase suggestions in real-time on every page load** — Searching through orders and line items for every product view is extremely expensive on WUs. Fix: Pre-compute related products nightly and store them in a Related_Products list field on each Product.
- **Showing the current product in its own suggestion list** — Recommending the product the user is already viewing wastes a suggestion slot. Fix: Add a constraint excluding the current product's Unique ID from all suggestion searches.
- **Not limiting the number of suggestions shown** — Too many suggestions overwhelm users and slow the page. Fix: Limit Repeating Groups to 4-8 items for suggestion sections.

## Best practices

- Pre-compute co-purchase data in nightly backend workflows for performance
- Exclude the current product from all suggestion Repeating Groups
- Limit suggestions to 4-8 products per section for visual clarity
- Use horizontal Repeating Groups for suggestion carousels
- Track product views to power recently viewed and personalized recommendations
- Allow admin curation of featured products for seasonal or promotional control
- Use tags for cross-category suggestions when products share attributes

## Frequently asked questions

### How many product views should I store?

Keep 90 days of ProductView records. Delete older ones in a weekly backend workflow to manage database size.

### Can I use AI for product recommendations?

Yes. Send user behavior data to an AI API via the API Connector for personalized recommendations. The AI returns product IDs that you display in a Repeating Group.

### How do I A/B test different suggestion algorithms?

Create an Option Set for suggestion methods. Assign users randomly to a method and track click-through rates per method to determine which performs better.

### Do product suggestions affect Workload Units?

Each search in a suggestion section consumes WUs. Pre-computing recommendations and storing them as list fields reduces WU consumption significantly.

### How do I track if users click on suggestions?

Create a SuggestionClick Data Type. When a user clicks a suggested product, log which suggestion section they clicked from. Analyze click-through rates per section.

### Can RapidDev build advanced recommendation engines?

Yes. RapidDev can integrate machine learning services for collaborative filtering, content-based recommendations, and real-time personalization in Bubble apps.

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Source: https://www.rapidevelopers.com/bubble-tutorial/set-up-product-suggestions-in-bubble
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