How to conduct user behavior analysis with AI in Bubble.io: Step-by-Step Guide

Create a Data Type called Activity with fields: User (User), Action_Type (Option Set: page_view, click, purchase, search, login), Page (text), Timestamp (date), and Metadata (text — optional JSON). In key workflows throughout your app, add Create a new Activity action. For example: Page is loaded → create Activity (page_view), Button clicked → create Activity (click), Purchase completed → create Activity (purchase). This builds a comprehensive activity log.

Go to Plugins → API Connector → Add another API named OpenAI. Set Authentication to Private key in header with key name Authorization and value Bearer YOUR_API_KEY (mark as Private). Create an API call named Analyze Behavior: POST to https://api.openai.com/v1/chat/completions. Body includes model (gpt-4o), messages array with a system prompt and user data. Set Use as: Action.

1POST https://api.openai.com/v1/chat/completions
2
3Headers:
4  Authorization: Bearer <API_KEY>
5  Content-Type: application/json
6
7Body:
8{
9  "model": "gpt-4o",
10  "messages": [
11    {"role": "system", "content": "You are a user behavior analyst. Analyze the following user activity data and provide: 1) A brief engagement summary, 2) Churn risk (Low/Medium/High) with reasoning, 3) Recommended actions."},
12    {"role": "user", "content": "<dynamic_activity_summary>"}
13  ],
14  "temperature": 0.3
15}

Create a backend workflow called analyze-user-behavior with a User parameter. In the workflow: (1) Search Activities for this user in the last 30 days. (2) Build a summary text: total activities count, login count, purchase count, most visited pages, days since last login. (3) Call the OpenAI Analyze Behavior action with this summary text. (4) Save the AI response to an Insight Data Type with fields: User, Summary (text), Churn_Risk (text), Recommendations (text), Generated_At (date). Schedule this workflow to run weekly for each active user.

Create an admin-insights page. Add a Repeating Group showing users with their latest Insight: User name, Churn Risk (with conditional coloring: red for High, yellow for Medium, green for Low), summary excerpt, and last activity date. Add a filter for churn risk level. Click a user to see their full AI analysis in a detail popup showing the complete summary, risk reasoning, and recommended actions.

Create a backend workflow that runs after each analysis. If the Churn_Risk is High, create a Notification for the admin team and optionally send an email alert. You can also trigger automated retention actions: send a re-engagement email to the user, offer a discount code, or flag them for a personal outreach call. Track whether these interventions work by comparing churn risk before and after.