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Case study · telecom

Tier-1 European telecom operator

AI deflection and AIOps cut support cost 35% while NPS improved 7 points across 18M subscribers.

AI SolutionsIntelligent AutomationBPO Services
Headline result
35% lower cost-to-serve
inbound contact volume reduction
38%
inbound contact volume reduction
47 min
MTTR (from 4.1 hours)
€28M
estimated retained annual revenue
The challenge

What was getting in the way.

A tier-1 European telecom with 18 million subscribers was absorbing 22 million support contacts annually — 61% of which were billing and usage queries answerable directly from self-serve data. Contact centre costs had risen 19% in two years with no corresponding improvement in customer satisfaction.

NPS sat at 28 — well below the category average. Network operations teams were processing 4,000 alerts daily through largely manual triage, leading to a mean-time-to-resolve of 4.1 hours on customer-impacting incidents. Monthly churn was 2.4%, with survey data pointing directly to billing confusion and poor incident communication as the primary drivers.

Pain points
A tier-1 European telecom with 18 million subscribers was absorbing 22 million support contacts annually — 61% of which were billing and usage queries answerable directly from self-serve data.
NPS sat at 28 — well below the category average.
Our solution

What we deployed.

Phase 1

Gennexa ran a simultaneous dual-track programme. On the CX track, NexaChat was deployed inside the operator's mobile app and web portal covering billing explanations, usage summaries, plan changes, and roaming queries across 8 languages. A proactive notification engine pushed personalised outage updates and usage alerts via push and SMS — getting ahead of inbound contacts before customers felt the need to call.

Phase 2

On the network track, AIOps models were trained on 36 months of alert history to distinguish genuine incidents from noise and classify probable root cause in under 60 seconds. An automated runbook engine resolved 43% of Tier-1 network alerts without human intervention. Engineers received remaining alerts with full context pre-loaded in a copilot interface, cutting investigation time dramatically.

Powered byNexaChatAIOpsNexaFlowProactive Notifications
Results

What we measured.

38%
inbound contact volume reduction
47 min
MTTR (from 4.1 hours)
€28M
estimated retained annual revenue

Inbound contact volume dropped 38% within six months of full deployment. Cost-to-serve fell 35% — the largest single-year improvement in the operator's history.

Mean-time-to-resolve on customer-impacting network incidents improved from 4.1 hours to 47 minutes. NPS improved from 28 to 35 — a 7-point lift. Monthly churn fell from 2.4% to 1.9%, translating to approximately €28M in retained annual contract revenue. The proactive notification engine alone deflected 2.1 million monthly inbound contacts.

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