Result from my work

Churn Case Study: Monthly Churn Down From 12% to 8%

By Vishwajeet Kumar · 5 min read · Updated 4 November 2026

Quick answerI brought monthly churn down from 12% to 8% in one year in a stockbroking platform by identifying at-risk users early and running targeted retention campaigns for them.

A stockbroking platform, Lead, CRM and Lifecycle Marketing (Oct 2024 to present). Figures are as reported on my resume, for the period stated.

The goal

A stockbroking platform with 12% monthly churn.

The aim: spot at-risk users early and bring monthly churn down with targeted retention campaigns.

The result

What I did and what changed

Result from my work

A stockbroking platform, Lead, CRM and Lifecycle Marketing (Oct 2024 to present)

Identified at-risk users early from their behaviour
Ran targeted retention campaigns for those users
Tracked churn every month

12% to 8%monthly churn in one year

-33%relative drop in monthly churn

Earlyat-risk users spotted before they left

Result as reported from my own work, measured over one year. The employer is not named, to respect confidentiality.

How I diagnose this

Define churn in a way the whole team agrees on
Find the behaviours that show up weeks before someone leaves
Split at-risk users by value and reason
Check which past retention efforts actually worked

The method behind it

The steps below are the general method. The details differ by company and product.

Define churn

One clear definition, tracked monthly by cohort.

Find the early signals

Falling activity, skipped sessions and ignored messages.

Score and segment

Rank users by risk and value so effort goes where it matters.

Step in with help first

Useful nudges and support before any offer.

Test what keeps people

Compare retention actions against a held-out group.

Review monthly

Keep what works, drop what does not.

A starting journey you can adapt

When	Channel	Message
Risk signal appears	Push	A helpful nudge tied to what the user usually does.
Day 2, no response	Email	A short guide or reminder of value, with one button.
Day 5, still quiet	WhatsApp	A personal check-in for opted-in users.
Day 10	Push	A final, relevant reason to come back, then pause.

Numbers to track

Monthly churn rate by cohortShare of at-risk users savedRetention at day 30 and day 90Revenue retained

Common pitfalls

Waiting until users have already left
Offering discounts before trying help
Treating all at-risk users the same

Frequently asked questions

How do you find at-risk users?

Look for early changes in behaviour, such as falling activity or skipped sessions, and score users by risk. The signals differ by product, so test them on your own data.

Does this work outside broking?

The method is general: define churn, spot early signals, step in with help and measure by cohort. The specific signals change by industry.

Services behind this work

Lifecycle Marketing

From first tap to loyal fan, and back again when they drift.

AI Growth Solutions

AI where it earns its keep: fewer manual hours, better messages and proof against a control group.