Telecom · Customer churn prediction
Banao builds subscriber-level churn prediction for telecom operators — scoring risk by usage pattern, care history, billing anomalies, and service mix, then surfacing the reason to the agent in your CRM before the subscriber calls to cancel.
The output is not a sorted list. It is a scored queue with reasons attached, integrated into your existing retention workflow, running against live BSS and CRM data rather than a monthly export.
A churn model that sits in a notebook does not reduce churn. We build the model, the workflow, and the integration — so the score reaches the agent who can act on it.
Each at-risk subscriber gets a risk score and the primary signal driving it — billing spike, service degradation, long silence from a high-ARPU account — so the retention agent's first call is informed rather than scripted.
Usage, payment history, care-contact frequency, plan tenure, and device age. We build the feature pipeline off your live data feeds, not a monthly flat-file export.
High-ARPU subscribers in a long-tenure silent phase look different from prepaid customers with a dropped auto-renewal. Separate cohorts get separate model logic and separate retention playbooks.
The score and reason surface in the system your agents already open — not a separate dashboard that only the analytics team watches. Integration is part of the build deliverable, not an add-on.
Subscriber behaviour shifts when you launch a new plan, a competitor changes pricing, or a market event hits. We instrument the model to alert when the feature distribution drifts so the score stays reliable.
Some subscribers the model flags as at-risk are ripe to upgrade if offered the right plan. We score both signals in one pass so the retention team can offer rather than only defend.
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
a European mobile operator
Static monthly reports listed churn candidates but reached agents too late — many had already submitted a port-out request. Banao rebuilt the scoring pipeline on live BSS feeds and wired the output into the agent desktop, so the queue populates daily with reasons attached.
Banao operates a ~300-person engineering company on its own AI products before any client sees them. InterviewGod screens our own hires. Vikaas runs our own demand generation pipeline.
A churn model that only works in a stable, clean dataset is not a production model. Running our operation on the same class of AI — where the data is messy and the decisions matter — means the version we deploy to your BSS has already been stress-tested on ours.
A model is only as good as what the business can do with its output. We will tell you before you fund a build:
We do not quote a churn model off a brief. We look at your actual subscriber data first.
We audit a sample of your BSS, CRM, and care-contact data, test whether the churn signal is learnable from what you have, and hand back a baseline accuracy estimate and ROI calculation — yours to keep either way. If you proceed, the Sprint cost is credited against the build.
Feature engineering from live BSS and CRM feeds, model training to your subscriber cohorts, and integration into the CRM system your retention agents use. The data pipeline is a deliverable, not a dependency left for your team.
Deployment with agent-facing UI, model monitoring for data drift, and daily score refresh. Retention-team onboarding is part of the deliverable — adoption and accuracy are both tracked.
How many historical churn events do you need to train a model?
It depends on your churn rate and subscriber base size. As a rough guide, several thousand confirmed churn events with matching subscriber history gives a workable starting point. The Discovery Sprint assesses whether your data volume supports a model or whether a cohort-based approach is more appropriate.
Can you work with our existing BSS and CRM systems?
Yes — legacy BSS, mediation layers, and custom CRM integrations are the normal case. We have built off flat-file exports, batch feeds, and direct database connections. Getting the data path working is part of the first two weeks of work.
Our BSS vendor already ships a churn analytics module. Why build something separate?
Vendor modules are trained on industry-wide patterns, not your subscriber base. They score the features the vendor exposes, not the signals specific to your network and care history. A custom model trained on your own data typically outperforms a pre-packaged score on your own churn cases — and integrates into the CRM workflow rather than sitting in a separate portal.
How do retention agents use the output day to day?
The score and reason surface in the agent's existing CRM view — a ranked queue of at-risk accounts, each tagged with the primary signal (billing, usage, silence, service complaint), so the first call is informed. Agents log outcomes, and those outcomes feed back into the model.
How long from start to a live model?
A typical path is a 2-week Discovery Sprint, a 6–8 week build, and a 2–3 week rollout with the retention team. Banao's ~300-engineer bench means delivery begins in days, not the months an internal hire would need to ramp.
Bring your BSS export and your last 12 months of port-out data. In 45 minutes we will tell you whether the signal supports a model — and what a retention integration would look like.
Book a 45-min scoping call
