The Greenscreens.ai Universal Batch Rating (UBR) feature lets you request predictions for up to 2500 lanes at once by uploading a spreadsheet or .csv.
A Universal Batch Rating provides you with a spreadsheet of rate predictions for your lanes, including:
Miles
Fuel Surcharge
Target Buy Rate
Low Rate
High Rate
Confidence Level
Network Rate
Network Confidence Level
Reach out to your Customer Success Manager with any questions, or for support with Universal Batch Ratings.
Getting a Universal Batch Rating
This looks like a lot of steps, but that's only because we've listed all of the many available options for customizing your batch prediction. It's actually pretty easy, as you'll see when you start to use it.
The Greenscreens.ai Universal Batch Rating (UBR) feature lets you request predictions for up to 2500 lanes at once by uploading a spreadsheet or .csv.
The short version is that, starting from the Batch tab in your Greenscreens UI, you'll be downloading a template, filling it out with lane information, and uploading it again.
At the top of your screen, click the Batch tab:
Under How it Works, click Download Template:
Open the template spreadsheet and fill in details on your lanes. The headers (load_id, pickup_date, etc.) need to stay the same. Some fields are required, and some are optional:
Required Fields
Optional Fields
Save your spreadsheet with a new file name, as either an .xslx or .csv file.
Back on the Greenscreens.ai Batch tab, select your upload options. To divide your report into tabs by region, toggle on Split Batch by Pages:
Click Choose and Upload File:
Select your .xlsx or .csv file.
If your download doesn't begin automatically, click Download.
Adding Historical Data to Your Universal Batch Rating
On the Greenscreens.ai Batch tab, when you select your upload options, toggle on Add Statistics to the Report:
Click the cog wheel icon to open the Output Configuration window:
NOTE: The historical data you select here will not appear unless Add Statistics to the Report is toggled on.
In the Output Configuration window, check any historical information you’d like to include for each predicted lane.
Select a geography range for your historical information. Note that this will apply not to your predictions, but only to any historical information you opt to include.
Select a date range for your historical information
Under Rate Mode, select whether to offer historical data by Flat Rate or Per Mile:
Click Save.
Upload your spreadsheet or .csv as in the instructions above.
Reading Your Batch Prediction
Your batch prediction will include:
Miles: the distance of the trip in miles
Fuel Surcharge: a prediction of fuel cost for the load based on market averages. The Fuel Surcharge is included in case you need to itemize costs for a customer. It may differ from the fuel cost already included in the Target Buy Rate, which is based on data from your specific brokerage.
Target Buy Rate: the predicted buy rate on a lane for your particular brokerage. This rate is influenced by your historical data and reflects your company’s individual buying behavior against the current market conditions, but is also often influenced by external data, such as the data we receive from the Greenscreens.ai network. It does include fuel cost.
Low Rate: the lowest rate that the AI predicts might result in a successful booking
High Rate: the highest rate that the AI predicts could be possible for the load
Confidence Level: an AI-generated score to help identify the amount of work that might be needed to find capacity at a specific price.
Network Rate: the expected rate in the market based on data from the Greenscreens network. It can be lower or higher than the Target Buy Rate, depending on how your brokerage books freight on the underlying lane or similar lanes.
Network Confidence Level: an AI-generated score representing the likelihood that any brokerage within the Greenscreens.ai network will be able to buy a specific lane at a specific price.
Any historical data you opt to include will also show up in new columns.
If the AI cannot provide a prediction for a particular lane, you'll find the reason the lane couldn't be calculated in the Description column.