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.
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GN Telecommunications Decision Pack · Telecommunications Decision Intelligence
Turn customer signals into the next retention or sales action.
For retention teams deciding which customers to contact and which treatment to offer. 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.
From today's process to the next decision
Choose who to contact and which retention offer to make.
How teams make the decision
Teams rank customers by churn risk or use broad CRM rules, then allocate retention offers and contact capacity.
What this leaves unresolved
High churn risk does not show who will change behaviour because of an offer. Discounts can go to customers who would stay anyway, while contact capacity is spent on customers unlikely to respond.
What the GN Pack adds
Estimate treatment effects where the data supports them, then combine expected uplift, customer value, offer cost and contact capacity to prioritise retention actions.
What your team receives
A ranked customer list with recommended treatment and contact priority, supported by intervention estimates where available and the economics behind the recommendation.
See the pack in action
From your business data to a decision you can act on.
Explore what a director or manager could receive. Choose a model, see its output, then compare practical actions with costs and constraints.
Illustrative interactive demoAll figures and businesses are fictional. These are examples of potential deliverables, not actual customer results, guarantees or live predictions.
TELECOMMUNICATIONS / EXAMPLE DECISION WORKSPACE
Who is worth retaining, and how?
Move beyond churn scores to decide which customer interventions actually justify their cost.
01 / Explore a model output
EXAMPLE OUTPUT 01 / 05
2,140 subscribers
Elevated 30-day churn risk
What we found
A risk score identifies likely departures, not whether giving a discount will change behaviour.
Recommended next move →
Separate risk from intervention eligibility.
Decision-relevant output2,140 elevated-risk subscribers
02 / Compare possible decisions
Who should receive a retention incentive?
Switch between the options and compare expected contribution after intervention costs. Numbers use fixed fictional assumptions.
Gross incremental contribution+€72,000
Intervention cost€26,000
Net incremental contribution+€46,000
Selected action
Optimised retention
Prioritise 420 customers with appropriate interventions.
Expected gross incremental retained contribution €72,000 less €26,000 incentives and contact costs.
Scope: 420 subscribersFeasibility: Within stated scenario rules
Operating constraints considered
See what this pack could recommend for your business →- Retention budget and contact capacity
- Customer suitability and eligibility
- Incremental retained margin net of incentive costs
Read every example output and decision alternative
Churn Risk Model: 2,140 subscribers — Elevated 30-day churn risk
A risk score identifies likely departures, not whether giving a discount will change behaviour. Separate risk from intervention eligibility. 2,140 elevated-risk subscribers.
Retention Uplift / Treatment Effect: 420 subscribers — Likely to benefit from an intervention
Estimated treatment response narrows the group worth contacting. Target eligible, persuadable customers rather than all high-risk subscribers. 420 candidate interventions.
Customer Value Model: 160 subscribers — Highest economic priority
These customers combine projected future contribution with credible incremental treatment benefit. Protect the priority segment within retention budgets. 160 priority accounts.
Cross-Sell / Propensity Model: 620 subscribers — Potential data-plan interest
Subscribers show elevated propensity for a relevant higher-data plan, subject to fit. Test suitable upgrade offers without assuming observed propensity is causal uplift. 620 upgrade candidates.
Next-Best-Action Optimisation: €46,000 — Expected incremental retained contribution
The recommended retention policy fits incentive and contact limits under the scenario assumptions. Reach selected subscribers with the appropriate action. +€46,000 modelled net contribution.
Decision options
Make no proactive retention offers. No incremental retained contribution or intervention spend. Scope: 0 subscribers.
Send the same discount to 2,140 high-risk subscribers. High gross benefit but substantial spending on customers who would stay anyway or cannot be persuaded. Scope: 2,140 subscribers.
Prioritise 420 customers with appropriate interventions. Expected gross incremental retained contribution €72,000 less €26,000 incentives and contact costs. Scope: 420 subscribers.
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.