Customer retention and churn · Planned
Who stays, who leaves,and when you notice.
Keeping a customer is usually easier than winning a new one. For that you need to see early who buys less or cancels. Peak Analytics is meant to show retention and churn from customer and subscription data. That is planned and not built. Today the preview shows how many orders come from returning customers and which clients no longer bring income.
One answer changes the follow-up
Pick a sample rating.
Local draft · no product activationPlanned, not available. Retention, churn and cohort evaluations are specified and not built; Peak Analytics reads no subscription or contract data. Examples are fictional.
02 / The follow-up to the data
And how sure are you which cohort stays the longest?
„Do most orders now come from regular customers?“ → Share of returning customers in a shared snapshot
Concept with fictional businesses · planned, not the product interface · not the product interface
Your question
How early do you notice today that a customer is drifting away?
Orders and receipts show who buys and pays. Who stopped and why isn't there: that needs customer or subscription data with a start and an end, and that source is planned. The example runs only in this browser window; the evaluation it describes is planned.
Local rule demo · Please use neutral sample details only.
Pick a rating to see the next step.
The answer sets the direction.
- Rating
- —
- Follow-up
- Waiting for a rating
No example picked yet.
The planned retention evaluation
Build cohorts, see who leaves, act in time.
The steps build on what the preview does today: pivot, finding, and „Check“ as a task.
- 01Step 1
Customers by start month
SaaS„Do autumn customers cancel faster than spring customers?“ → Weakest cohort as a task for customer success
Peak Analytics is meant to group customers by the month of their first purchase or contract, so each cohort shows on its own.
Read subscription data as a source - 02Step 2
Measure staying and leaving
0–6Follow-up7–10FinishEach cohort is meant to show how many still buy or pay after one, three and six months, and how many have cancelled.
Start month × months since start - 03Step 3
Hand at-risk customers to someone
Shop„Do most orders now come from regular customers?“ → Share of returning customers in a shared snapshot
A cohort or customer that stands out is meant to land as a task in Peak Work with „Check“, for example with the customer success team.
Weakest cohort as a task for customer success
What already exists today
The Peak Analytics preview already exists.
This page describes a planned capability. In the preview today: pivot evaluations over orders, receipts, tasks, goals and files, the largest value with its share, „Check“ as a task, CSV export and sharing a released snapshot. The planned part is meant to build on that. Tasks from Peak Work are one of the five Peak Analytics sources, and „Check“ turns a finding into a task of its own there, with a link back to the evaluation.
Look at Peak WorkPeak Analytics / Your next step
Measure loyalty before the customer is gone.
See what the planned retention evaluation is meant to look like, and what orders and receipts already show today.
See the planned retention evaluation
Measure loyalty before the customer is gone.
„Do most orders now come from regular customers?“ → Share of returning customers in a shared snapshot
Frequently asked questions
Can I use this in Peak Analytics yet?
No. This capability is planned and not built yet; there is no launch date. Available today is the Peak Analytics preview: pivot evaluations over orders, receipts, tasks, goals and files with CSV export and sharing.
Can I see a churn rate today?
No. Churn needs customer or subscription data with a start and an end. A rate from orders or receipts would be a guess and isn't shown.
What does the preview show on retention?
Orders per month by new and returning customer, and income per counterparty and month, where empty months stay visible.
Is this the same page as churn reduction?
Yes. Retention and churn are two sides of the same question and are combined here.