Predictive Lifetime Value Modeling in Multi-Channel Telecom Customer Systems

Abstract

Telecom operators estimate customer worth from measures that look backwards. Current monthly revenue, average revenue per user, contract value, tenure, product count, payment history and recent usage are combined into high, medium and low value bands that mostly restate past spend. The result is a position, not a direction, and it says nothing about how value will move over the next one to three years. The deeper problem is not the choice of estimator. It is that the evidence is fragmented: billing, customer relationship management, digital engagement, customer service, network usage, partner channels and campaign response are each evaluated on their own, so the signals that would together indicate a change in trajectory are never combined. This paper describes a lifetime value modelling system built on a multi-channel customer profile rather than on a single system of record, and it treats the construction of that profile as the substantive engineering problem. Identity is resolved through a governed ladder: deterministic matching on stable identifiers, then controlled probabilistic matching whose confidence must clear a threshold before any merge occurs, and ambiguous records that are deliberately not merged but retained separately or held at lower confidence until stronger evidence arrives. Source-system lineage is preserved so every model feature traces to the system it came from. Channel-specific activity is normalised into one behavioural event vocabulary in which every event keeps its timestamp, source channel, product context and outcome, which is what makes cross-channel patterns visible: declining network usage, repeated contacts about one unresolved issue, an ignored retention email and a plan comparison on the website together indicate falling future value, though no single system holds the whole pattern. Features are then built from trajectory rather than snapshot, and predicted value is expressed as expected future revenue multiplied by retention probability less expected cost to serve, over twelve, twenty four and thirty six month horizons. Two governance positions are argued as design principles rather than afterthoughts. Engagement measured on the digital subset systematically understates customers who transact through stores, partners and the contact centre, so engagement is modelled across every channel. And economic prediction is separated from service entitlement: predicted value may determine which retention offer or cross-sell is appropriate, but it never determines who receives essential support, complaint resolution or network assistance, and a low predicted value never lowers service quality. Business figures are reported as pilot and implementation ranges rather than audited production results, and no model accuracy figures were available, so none are reported.