How many tasks per board are in which status?
Orders · Rows channel, columns month, sum of revenue · Largest value: online shop in November
Pivot analysis · Preview
The pivot evaluation is the core of Peak Analytics. You pick a source, decide what goes into rows and what into columns, and which measure is calculated: count, sum, average, minimum or maximum, on a grid from day to year.
Build a sample pivotIn the preview for orders, receipts, tasks, goals and files. Sample numbers on this page are fictional.
01 / Example value
Which question would you build as a pivot first?
Orders · Rows channel, columns month, sum of revenue · Largest value: online shop in November
Change the example value. The finding follows the value.
Fictional example · no real data
Two sample questions
What goes into the rows, what into the columns?
How many tasks per board are in which status?
Orders · Rows channel, columns month, sum of revenue · Largest value: online shop in November
Tasks · Rows board, columns status, count · Largest value: board “Relaunch”, in progress
Honest about the status
01 / What is settled today
Peak Analytics is a preview. You can evaluate orders, receipts, tasks, goals and files, not arbitrary external data.
02 / Your control
Businesses and numbers on this page are made up and marked as examples. None of it is a measurement or a customer result.
Compare both evaluations and their reasons.
03 / The clear limit
The website reads no data from your workspace. Evaluations only happen in Peak Analytics, with your permissions.
Your next step
Build the sample pivot and see how rows, columns and measure add up to an answer.
Build a sample pivotIn the preview for orders, receipts, tasks, goals and files. Sample numbers on this page are fictional.
Peak Analytics is available as a preview. What this page describes as available exists in the preview; examples with company names and numbers are fictional.
Count, sum, average, minimum and maximum. Date fields are grouped by day, week, month, quarter or year.
Empty, null and missing stay distinct. They don't silently drop out of the sum.
Then Peak Analytics evaluates a cut-out and marks it as such, so you don't mistake part of the data for all of it.
Yes. A saved evaluation is a saved view; the question lives in its address, so others work with the same definition.