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Telecom
The customer who leaves quietly costs more than the one who complains.
Telecom churn is rarely a surprise to the data and almost always a surprise to the organisation, because the signals are spread across network, billing, care and app systems that never speak to each other. We connect them, then act on them.
−31%
churnAttrition reduction with decision policy
−54%
costCost per customer interaction
−19%
discountingRetention spend removed from safe customers
Six teams, six fragments, one customer walking out.
We traced a single churned subscriber through an operator's estate. Care held two unresolved tickets. The network team had a cell-site fault at her address. Billing had increased her tariff. The app team had watched her sessions fall to zero. Marketing had sent an upgrade offer four days before she ported out. Every team had done its job with the data it owned.
Nobody could see the composite picture: a customer being pushed out by the accumulated behaviour of the company. A better churn model would not have helped — they already had one with 91% accuracy, and churn had not moved, because the output went to a team that discounted everyone on the list.
We built the shared context layer and the decision policy on top of it: who is worth saving on margin, who responds to contact at all, what the cheapest effective intervention is, and a holdout group so the value was provable. Churn fell 31% and retention spend fell too. The model was never the missing piece.
For the first time our retention team and our network team were arguing about the same customer, with the same facts.
Six telecom value pools we work in repeatedly.
High volume, thin margin per subscriber and enormous signal density — which makes telecom one of the highest-return sectors for applied AI.
Churn prediction & save policy
Attrition scores with reason codes, combined with a policy that selects the cheapest effective save action per subscriber and leaves safe customers alone.
Voice AI & care automation
Multilingual voice agents handling balance, tariff, activation, fault and billing intents, with context-rich escalation to human agents.
Revenue assurance & leakage
Detect rating errors, unbilled usage, provisioning mismatches and reconciliation gaps between network, mediation and billing.
Next best offer & CVM
Personalised upsell, cross-sell and recharge prompts chosen on expected margin, contact fatigue and channel preference rather than campaign calendar.
Network experience correlation
Connect network quality at subscriber level to complaints, usage decline and churn, so engineering investment can be prioritised on revenue at risk.
Fraud & SIM abuse detection
Subscription fraud, SIM-box bypass, wangiri and promotion abuse detected on behavioural patterns rather than static rules.
What moves in a telecom value model.
Each of these is written into the engagement as a number with an owner, a baseline and a review date.
OUTCOME 01
Churn falls and save spend falls with it
Targeting retention on margin and responsiveness rather than on risk score alone reduces attrition while removing discounting from customers who were never going to leave.
OUTCOME 02
Care cost drops without service damage
Routine intents resolve without a human and human agents handle more per hour with assist. Satisfaction typically improves because nothing waits in a queue.
OUTCOME 03
Leakage becomes visible
Continuous reconciliation between network, mediation and billing consistently surfaces revenue between 0.4% and 1.8% of turnover that nobody knew was missing.
Built for telecom scale.
- Subscriber-level context layer across network, billing, care and digital
- Churn, propensity and fraud models with reason codes
- Decision policy with contact fatigue, eligibility and budget constraints
- Voice and digital agents in your local languages
- Holdout measurement framework for every campaign and save action
- Monthly value reporting on churn, ARPU and cost per interaction
The engineering underneath.
Numbers from work already in production.
Measured against the baseline agreed with the client before the engagement started.
−31%
Subscriber churnDecision policy on an existing churn model
+41%
Care containmentVoice AI across six high-volume intents
0.4–1.8%
Revenue leakage recoveredContinuous assurance reconciliation
What operators ask.
- 01. We already have a churn model. What would you add?Almost certainly the decision and measurement layers. Most operators we meet have adequate prediction and no policy for acting on it, which is why the accuracy never converts into retained subscribers.
- 02. Can you work with our existing CVM platform?Yes. We frequently supply the intelligence and decision layer into an incumbent campaign platform rather than replacing it, which is faster and considerably cheaper.
- 03. Do you handle local languages and code-switching?Yes, including mixed-language conversation, which is how subscribers in South Asia actually speak. English, Hindi and Nepali are first-class in our stack.
- 04. How do you prove the churn reduction was yours?Randomised holdouts. A portion of the eligible base receives no intervention, so the reported reduction is incremental rather than coincident with tariff changes or market movement.
Where to go next.
Give us one churned subscriber. We will show you how many systems saw it coming.
The assessment traces real churned subscribers through your estate and quantifies what unified context and a save policy would be worth.
