Who should we intervene with?
Combine churn risk, expected treatment effect, customer value and intervention economics so retention capacity is focused where it can actually change behaviour.
← All Decision Packs
GN Telecommunications Decision Pack · Telecommunications Decision Intelligence
Turn customer signals into the next retention or sales action.
Use customer, product and interaction data to decide who to retain, cross-sell or prioritise when customer value and commercial capacity are constrained.
Decisions this pack supports
Score the opportunity, then route the action.
Which offer fits which customer?
Use product gaps, propensity and customer context to prioritise relevant next offers.
Who should receive scarce outreach first?
Rank customers or leads by likelihood, value and business constraints before allocating contact capacity.
Models inside this pack
One product bundle around a recurring business decision.
GN Telecommunications combines the models, business logic and constraints needed to move from your operating data to a decision-ready recommendation.
DECISION PACKGN Telecommunications
Built aroundCustomers · products · usage · interactions · value
Churn Risk ModelEstimates which customers are most likely to leave or reduce engagement.
Retention Uplift / Treatment EffectEstimates which customers are likely to change behaviour because of a retention action rather than leave or stay anyway.
Customer Value ModelEstimates expected future customer value and combines it with cost-to-serve or offer economics.
Cross-Sell / Propensity ModelScores product gaps and likelihood to accept the next relevant plan, add-on or service.
Next-Best-Action OptimisationCombines risk, uplift, value, offer economics and contact capacity.
OUTPUTDecision support your team can use
Models are combined only where the decision needs them. The pack returns a business-ready action with supporting evidence and expected impact.
- Customer
- offer
- retention
- sales priority
- Expected impact and supporting evidence
- Configured around your real operating constraints
Churn Risk Model
Estimates which customers are most likely to leave or reduce engagement.
Where retention attention may be needed, before intervention economics are considered.
Retention Uplift / Treatment Effect
Estimates which customers are likely to change behaviour because of a retention action rather than leave or stay anyway.
Who should actually receive an intervention, and who should not be over-discounted.
Customer Value Model
Estimates expected future customer value and combines it with cost-to-serve or offer economics.
How much retention or sales effort is economically justified.
Cross-Sell / Propensity Model
Scores product gaps and likelihood to accept the next relevant plan, add-on or service.
Which offer fits which customer.
Next-Best-Action Optimisation
Combines risk, uplift, value, offer economics and contact capacity.
Which customer should receive which action first.
Pack anatomy
One repeatable path from customer data to action.
Reusable scoring and decision workflows, configured around your offers, economics and capacity.
Your customer data
Customer history, products, usage, interactions, channels, campaigns, value and relevant service or network signals.
Predictive + value models
Use churn, propensity, segmentation, customer value and forecasting models as required by the decision.
Offers, economics and capacity
Encode the available offers, contact capacity, customer value and intervention rules that make prioritisation realistic.
A ranked customer action
Return the customer, offer, priority and supporting evidence into sales, retention or CRM workflows.
Evidence
From broad outreach to ML-driven prioritisation.
In a published True Corporation case study, Graphite Note was used to score leads, match product gaps and rank the call list for a tele-sales use case.
Read the full case studyPublished outcome
3× conversion-rate improvement
The case study reports conversion rising from 1.9% to 5.2% while operational time fell 77%.
Direct answers
Questions about the GN Telecommunications Decision Pack.
What is the GN Telecommunications Decision Pack?
It is a packaged Decision Intelligence solution for recurring telco decisions around churn, customer value, lead scoring, cross-sell and commercial prioritisation.
What data does it use?
Typical inputs include customer history, products, usage, interactions, campaign data, value and available service or network signals.
Is this only a churn model?
No. Churn prediction can be one input, but the pack is designed around the action that follows: who is worth retaining, who should receive outreach and which offer is appropriate under real constraints.
Why not simply contact the customers with the highest churn risk?
Because high churn risk is not the same as high retention uplift. Some customers would leave despite an offer, while others would stay without one. Where the data supports it, Graphite Note can estimate treatment effect and combine it with customer value, offer cost and contact capacity so retention effort is aimed at customers whose behaviour is more likely to change.
What comes out?
The output can be a ranked customer list, next-best offer, retention priority, segment or other commercial action ready to enter the existing workflow.
Decision IntelligenceFrom signal and prediction to a governed business action.AutoMLAutomate repetitive model-building work without losing business context.Causal Machine LearningSeparate drivers from correlations and estimate intervention effects.Predictive AnalyticsForecast demand, score customers and anticipate business outcomes.Decision OptimisationCompare feasible actions and rank them by expected business impact.Forecasting vs OptimisationSee where prediction ends and the business decision begins.Decision APIDeliver recommendations, expected impact and evidence into workflows.Embedded Decision IntelligenceAdd a decision layer to ERP, CRM, BI, workflow and vertical software.Build vs BuyA practical framework for deciding whether to build, buy or embed.
Bring the customer decision and the capacity constraint.
We will structure the pack around the commercial action that follows.