What the data for this category looks like
Typical landing page conversion optimization work serves growth product managers, front-end operations specialists, and ad optimization specialists. Daily documents include landing page plans, tracking requirement documents, A/B test reports, and form field lists. Data sources include landing page access logs, heatmaps, form data, and ad UTM parameters. Update frequency is weekly or per individual campaign. Data fields include page path, form submission content, ad UTM parameters, session identifiers, and more. Only post-ad-click page engagement data is processed, with a clear separation from the ad delivery effect optimization workflow.
What constraints do these characteristics impose on deployment and upgrades
The data workflow for landing page conversion optimization links ad delivery and page engagement stages. During deployment, ensure the full workflow of ad parameter collection, page request validation, and form data storage is complete. Avoid lead loss or data attribution failures. Because data updates happen weekly or per campaign, upgrade operations must not interrupt page engagement services for active campaigns. Maintain normal business operations. The variety of daily documents and data fields requires deployment configurations to flexibly adapt to custom fields and path rules for different landing pages. Also ensure log collection stability. Prevent unnecessary increases in ad costs caused by data errors.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 1000–1500 records | Adapts to thousands of weekly landing page log collection volumes, prevents database connection pool overflow |
REDIS_URL | Configure per deployment environment; set session timeout to 30 seconds | Caches ad source parameters and form session data. The timeout setting balances resource usage and session retention requirements |
FE_DOMAIN | Official domain corresponding to the landing page, such as https://lp.example.com | Validates the legitimacy of landing page requests, blocks cross-domain unauthorized calls, and ensures normal ad parameter collection |
STORAGE_S3_ENDPOINT | Configure using standard endpoints provided by the object storage service provider | Stores landing page heatmaps, access logs, and form attachments. Adapts to storage requirements for different cloud environments |
FILE_TOKEN_KEY | Randomly generated 32-bit string | Encrypts access tokens for landing page attachments. Prevents unauthorized file downloads and data leaks |
Three common mistakes
- Phenomenon: Variables splice correctly during workflow text debugging, but fail to return when called via API. Cause:
REDIS_URLwas not configured correctly during deployment. Session variable cache fails, and API calls cannot read temporarily spliced parameters. - Phenomenon: Containers remain in a loading loop after Docker desktop deployment. Cause:
MONGODB_URIorPG_URLis not filled in correctly. Database connection failure blocks service initialization. - Phenomenon: Ad source parameter fields are empty in landing page access logs. Cause:
FE_DOMAINis not configured during deployment. Cross-domain requests are blocked, and UTM parameters from ad platforms cannot be collected normally.
How to confirm the configuration is correct
- Run a local API call test using FastGPT v4.17.0. Pass preset text splicing and variable parameters. Check if the returned result includes the expected variable content.
- View Docker container runtime logs. Confirm there are no error messages for database connections or cache service connections. Confirm the service initialization process completed normally.
- Access the target landing page and submit a test form. Check the log module in the backend management interface. Confirm ad source parameters and form field data are recorded normally.
- Generate an access token for a test file. Access the uploaded test file via the token link. Confirm there are no permission errors and the file loads normally.
The values given are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.