Bulk Import of Forecasts

Updated 

You can bulk import forecast files using the SFTP. Simply multi-select the Excel files you wish to import, then drag and drop them into the uploads/forecast-import directory on the SFTP. Once processed, these files will appear in draft status on the forecasting records manager page.

Prerequisites

Creating import forecast files in the required format:

Column headers

  

workload is the unique sprinklr ID for each workload created on the platform. Populate the same workload ID(s) throughout the sheet for which you are creating the forecast.  

startTime is the timestamp against which the parameters were recorded. The format of this cell should always be typed as dd/mm/yyyy hh:mm AM/PM.

Dates: Supports various formats including DD/MM/YYYY, DD/MM/YY, MM/DD/YYYY, MM/DD/YY, YY/MM/DD, YYYY/MM/DD, three-letter month abbreviations (e.g., Aug, May), and '-' delimiter.

Times: Supports formats such as HH:MM, HH, HHMM, and AM/PM.

volume, AHT, and serviceLevel columns: Users can import forecasts based on additional parameters, supporting multi-parameter forecasting.

Connect to the SFTP Server

To get this capability enabled, please raise a support ticket by contacting tickets@sprinklr.com.

Bulk Import Excel Files

  1. Multi-select the Excel files you wish to bulk import.

  2. Drag and drop these files into the uploads/forecast-import directory on the SFTP.

  3. After being bulk imported from the SFTP directory, the forecast files are processed and reflected in draft status on the forecasting records manager page. The names of the imported forecasts follow the format: filename_date_timestamp.

  4. These forecasts will only be visible to those users with whom the workload is shared with, while setting up the workloads. Please note that a forecast should not be created with multiple workloads that are shared with different users.

  5. Click the eye icon next to the forecast to navigate to the forecast landing page.

    Select a single day from the date range selector to view the forecast at granular intervals.