Run your first baseline
This lab walks a new user through the baseline tool so they know what to upload, which settings matter, and what to look for in the results.
Step-by-step workflow
- 1
Open the baseline tool and load telemetry
From the home workspace, open the Baseline & Event Performance Calculator. In Step 1, upload your interval CSV. Confirm the uploaded data preview looks plausible before you continue.
- Check the first and last timestamps.
- Look for obvious timezone or missing-interval issues.
- If the preview looks wrong, stop and correct data quality first.
- 2
Define the event window
In Step 2, enter the event date, start time, and duration. Treat this as the exact period you want the platform to compare against historical candidate days.
- Use the real operational event window, not a rough approximation.
- Keep the direction of the event in mind for later performance interpretation.
- 3
Select the baseline logic
In Step 3, choose the methodology that fits your use case. For a first pass, start with a familiar option like X of Y or a day-matching average.
- Set candidate depth thoughtfully.
- If using X of Y, make sure you understand whether you want high-of-Y or middle-of-Y logic.
- 4
Set preferences and same-day adjustment
In Step 4, confirm timestamp convention, similar-day rule, event direction, and whether holidays should be excluded. If you enable SDA, define a window and caps/factors that you can explain later.
- Keep SDA disabled on the first run if you want a cleaner baseline-only comparison.
- Enable SDA on the second run and compare how it changes the event story.
- 5
Generate and review results
Generate the baseline and inspect all three layers of output: candidate-day rankings, the charted baseline versus actuals, and the summary metrics.
- Look at which candidate days were selected.
- Check whether the event shape makes operational sense.
- Note the average delivered event performance and event energy effect.
- 6
Export what you need
Download the baseline intervals CSV, audit CSV, or JSON bundle if you need to review the run outside the UI or share it with another stakeholder.
- Use the audit export when you need traceability.
- Use the JSON bundle when you want the richest machine-readable package.