Begin by accessing the Intercom API to extract the necessary data. Intercom provides a RESTful API which you can query to get the data you need. Use an HTTP client (such as `curl` or a programming language like Python with `requests` library) to send GET requests to the Intercom API endpoints. Ensure you have the correct API credentials and handle authentication as required by Intercom's API documentation.

Once you have retrieved the JSON data from Intercom, you need to transform it into a CSV format since Amazon Redshift easily ingests CSV files. Use a scripting language such as Python to parse the JSON and write the data into a CSV file. Libraries like `pandas` in Python can simplify this transformation process by allowing you to normalize JSON data and convert it directly into a CSV file.

You need to have an Amazon S3 bucket set up to temporarily store the CSV files before loading them into Redshift. Create a dedicated S3 bucket or use an existing one, ensuring that you have the correct permissions set up. You’ll need to allow both read and write access to the bucket for the user account that will be performing the data load.

Use AWS CLI, Boto3 (Python's AWS SDK), or another method to upload the CSV files to your S3 bucket. Ensure that the files are correctly uploaded and accessible. Verify the upload by listing the contents of your S3 bucket using the AWS Management Console or the AWS CLI.

Before loading data into Redshift, ensure that you have a table set up that matches the schema of the CSV files. Use the Amazon Redshift console or SQL client to connect to your Redshift cluster and create a table with the appropriate columns and data types to store the data from Intercom.

Use the Redshift `COPY` command to load the CSV data from your S3 bucket into the Redshift table. This command efficiently copies the data into Redshift and requires you to provide the S3 path, IAM role with access permissions, and any necessary options like CSV format and delimiter settings.

Once the data is loaded into Redshift, perform a data integrity check to ensure that the data was transferred accurately and completely. Run SQL queries to verify the row counts and data accuracy. After verification, clean up by deleting the temporary CSV files from your S3 bucket if they are no longer needed, to avoid unnecessary storage costs. Additionally, consider implementing logging and monitoring to keep track of the data transfer process.
By following these steps, you can efficiently transfer data from Intercom to Amazon Redshift without relying on third-party connectors or integrations.