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Arcade Alliance

Live demo · AI tool · Subscription businesses, gyms & lenders

Know who might leave while you can still keep them.

Every customer is scored weekly from data you already have. The riskiest come with their reasons and a suggested action, and the model is checked each month against who really left.

Try it live Make it yours

The problem

“We find out a customer has left only when they don’t renew.”
The problem this demo is built to solveLive demo · sample data

What is customer churn prediction?

Customer churn prediction uses a business’s own data — usage, complaints, payments, plan history — to score how likely each customer is to leave in the coming weeks. A useful model explains each score, suggests an action such as a call, an offer or a technician visit, and is checked every month against who actually left.

Built around your workflow: this demo is a starting point — screens, fields, rules and integrations are made to fit your business.

Knowing a customer may leave

Before: At cancellationWith the system: Up to 60 days ahead, with reasons

How the system works · how we measured

In the demo each customer is scored for the next 60 days with the main reasons; how many leavers a built model catches is measured monthly on your own data.

01 · The problem

What problem does it solve?

A regional broadband provider with 12,000 home and business customers loses a steady number each month. The team learns about it when the cancellation request arrives — too late to fix the slow connection that caused it.

Retention offers go to everyone at renewal, including customers who were never going to leave.

  • Silent departures

    Customers leave without complaining twice.

  • Wasted discounts

    Offers to people who would have stayed.

  • Wrong fixes

    Discounts where a technician was needed.

  • Late warning

    Found at cancellation.

02 · The solution

How does Customer Churn Prediction fix it?

  1. 01

    Learn from who left

    The model studies past leavers’ usage, tickets, payments and plans to find early signs.

  2. 02

    Score every week

    Each customer gets a risk score for the next 60 days.

  3. 03

    Explain and suggest

    The top reasons and the action most likely to help: a call, an offer or a technician.

  4. 04

    Keep it honest

    Each month, last month’s scores are checked against who actually left, and the model is retrained.

03 · The result

What changes?

Knowing a customer may leave

Before: At cancellationWith the system: Up to 60 days ahead, with reasons

How the system works · how we measured

In the demo each customer is scored for the next 60 days with the main reasons; how many leavers a built model catches is measured monthly on your own data.

Score for every customer
Weekly
Behind each score
Reasons
Suggested for each
Action
Monthly against reality
Checked

Live demo

Try it live.

A scripted demo with sample data, so it behaves the same for everyone. Nothing you do here is saved or sent anywhere.
  1. 1Look at why each customer is at risk.
  2. 2Act on all high-risk customers at once.
  3. 3Run the Monday scoring and see the risk bands.
Make it yours
Loading the demo…

Before & after

The same day, two ways.

Watch each task move from the way it’s done today to the way it runs on the system — or drag the slider yourself.
Today
With the system
  • WarningToday: The cancellation requestWith the system: A risk score weeks ahead
  • WhyToday: GuessedWith the system: Reasons with each score
  • OffersToday: To everyone at renewalWith the system: To the at-risk, by reason
  • Model qualityToday: —With the system: Checked monthly

Your numbers

What could it save you?

Put in your own volume and timing. It’s your estimate, worked out in your browser — not a promise from us.
min
min

Our estimate for the same task — change it if you like.

₹ / hr

Your estimate

8 hours a month

About 1 working days back every month.

120 retention calls a month × 4.0 minutes saved each. Your numbers stay in this browser.

Make it yours

Built around your business.

Choose what your version of Customer Churn Prediction needs. Your brief builds itself — send it, and we reply with a plan.

Often asked for by businesses like yours

  • What “leaving” means for you
  • How far ahead to predict
  • Actions you can afford
  • Segments to score separately
  • Who sees the scores
Always included
  • Risk scores
  • Reasons
  • Suggested actions
  • Monthly accuracy check
Add modules
Connect to the tools you use
Runs on
Languages
Hosting
Team size

Under the hood

How is it built?

Where the data comes from, what the system does with it, and what comes out.

Inputs

  • Usage & activity
  • Tickets & complaints
  • Payments & plan history

The engine

  • Gradient-boosted risk model
  • Reason codes per customer
  • Action rules

Outputs

  • Weekly risk lists
  • Suggested actions
  • Monthly accuracy report

Modules

  • Risk score per customer
  • Reasons shown
  • Suggested actions
  • Retention lists
  • Weekly scoring
  • Accuracy check each month

Connects to

  • Your billing system
  • CRM
  • Ticketing / helpdesk
  • WhatsApp Business API
  • Data warehouse

Stack

  • Python (scikit-learn / XGBoost)
  • SHAP for reasons
  • Your CRM & billing data
  • PostgreSQL / BigQuery
  • Scheduled jobs
  • CRM or WhatsApp for actions

Proof, the honest way

What would you measure?

This is a demo, so we don’t claim client results. In a real roll-out, these are the numbers we track from week one — before and after.

  • Leavers caught

    Share of leavers flagged in advance.

  • False alarms

    Flagged customers who were never at risk.

  • Saves

    At-risk customers who stayed after an action.

  • Cost per save

    Offers and visits per customer kept.

FAQ

Customer Churn Prediction FAQ

All questions →
What is customer churn prediction?

Customer churn prediction uses a business’s own data — usage, complaints, payments, plan history — to score how likely each customer is to leave in the coming weeks. A useful model explains each score, suggests an action such as a call, an offer or a technician visit, and is checked every month against who actually left.

How much data do we need?

Usually at least a year of customer history with a few hundred customers who left. We check your data before promising anything.

How accurate is churn prediction?

It depends on your data. We measure it on customers the model has never seen, report it plainly, and re-check it every month.

Why show reasons?

A score alone isn’t actionable. Knowing it’s slow speed rather than price tells the team to send a technician, not a discount.

Is customers’ data safe?

Models run on your servers or a private cloud; personal data is minimised and access is limited to the retention team.

Can it work for gyms or lenders?

Yes — the approach is the same; the signals differ (visits for gyms, repayments and usage for lenders).

Built around you

Every demo is a starting point.

Nothing here is sold as-is. Each one is rebuilt around your business — these are the things we tailor on every project.

  • Your workflow

    Steps, approvals and statuses the way your team works today.

  • Roles & permissions

    Who sees and changes what — by branch, team or person.

  • Fields & forms

    Your terms, your fields, your numbering and units.

  • Reports

    The numbers you check every day, in your format.

  • Integrations

    Tally, WhatsApp, payments, couriers and the tools you already use.

  • Your brand

    Your name, logo and colours on every screen and document.

  • Language

    English, Hindi or a regional language for staff and customers.

  • Hosting

    Our cloud, your cloud account or your own server.

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Updated 30 Oct 2026 · Arcade Alliance Network is a web, app, software and AI development company in Lucknow, India, building custom software for businesses in India and worldwide.

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