Reference
Review the setup requirements and sequence, and find the guides to use at each stage.
Connect the Context API to your server or CRM to use installation attribution and purchase and churn predictions for each app user. Confirm that CUE is enabled and prediction data is ready for the target app. Then prepare the app's token and a test user ID.
Connect the Context API
Prepare your app, send a request, and handle the response in this order:
- Follow Get Started to check your app settings and prepare a token and a test user ID.
- Build a request using the Endpoint, Headers, Request body, and Payload examples sections in Context API.
- Check
appId,user,context.lastTouchpoint, and the purchase and churn predictions in a successful response. - Follow Error Handling to implement default behavior for errors and timeouts.
- Test successful responses, responses that do not meet your service's conditions, and failed lookups. Check that your service displays the intended screen or sends the intended message in each case.
Related guides
Use these guides to find the details you need while connecting the Context API.
| Document | What it covers |
|---|---|
| Context API | Explains how to choose one of three identifiers, with complete request and response examples and field definitions. |
| Error Handling | Explains the causes of 12 error codes, how to handle them, when to retry, and what to include when requesting help. |
| Using Predictions | Explains prediction windows, probabilities, and tiers for purchase and churn. |
| Glossary | Defines tokens, identifier mappings, JSON, and default behavior. |
Documentation and API specification
Successful Context API responses contain requestId, appId, user, context, and predictions. See Context API and the OpenAPI specification for the complete response and field definitions.
The context.lastTouchpoint object contains information about the ad credited with the installation. Using Installation Attribution explains how to use that information for personalization. Using Predictions explains the purchase and churn prediction windows, probabilities, and tiers returned in predictions.
If you are an agent reading this reference, start with llms.txt to find the guides and setup requirements relevant to your task. Read each page's Markdown source, and use the OpenAPI specification for request and response schemas.