Skip to content

Dynamic Pricing & Offer Management

Personalized plan recommendations, contextual offers, and price elasticity optimization for telecom commercial teams.

Priority: P2 — Strategic Value
Time to Value: 8-10 weeks
Category: Commercial & Revenue Growth

Business Problem

Telco pricing has shifted from simple voice/SMS plans to complex bundles combining data, streaming, devices, and value-added services. Traditional pricing approaches cannot keep pace:

  • One-size-fits-all plans — a limited portfolio of 5-10 plans serves millions of subscribers with vastly different usage patterns, leaving value on the table for both the operator and the customer
  • Slow plan changes — new plans take weeks to design, configure in BSS, and launch; competitors respond faster
  • No personalization — two subscribers paying the same price may have completely different usage profiles; neither is optimally served
  • Price sensitivity blindness — pricing decisions are based on competitor benchmarking and gut feel, not on measured elasticity per segment
  • Offer fatigue — subscribers are bombarded with generic promotional offers, leading to declining response rates and brand dilution
  • Bundling without data — device + plan + VAS bundles are designed based on commercial negotiations rather than subscriber preference data

Capabilities

Personalized Plan Recommendation

AI model matching each subscriber's actual usage pattern (data heavy, voice heavy, roamer, streamer) to the optimal plan from the current portfolio — surfacing savings or better-fit options in self-service channels.

Contextual Offer Engine

Real-time, trigger-based offers delivered at the right moment: subscriber runs out of data → instant data booster offer; subscriber travels internationally → roaming pack push; subscriber's contract expires → personalized renewal offer.

Price Elasticity Modeling

Measure price-response curves per segment: how does plan uptake change at different price points? Identify revenue-maximizing price levels for each plan × segment combination.

Dynamic Bundle Design

Data-driven identification of optimal product combinations (plan + device + streaming + insurance) that maximize subscriber lifetime value and reduce churn through multi-product stickiness.

Competitive Pricing Intelligence

Monitor competitor plan offerings, pricing changes, and promotional activity. Alert commercial teams when competitive gaps emerge and model the impact of pricing responses.

Data Sources & Ontology Mapping

flowchart LR
    subgraph Data Plane
        BSS["BSS / Billing"]
        CRM_SYS["CRM"]
        CDR_SYS["CDR / Network Data"]
    end

    subgraph Ontology Entities
        SUB["Subscriber Profile"]
        PLANS["Plan & Pricing"]
        USAGE["Usage Behavior"]
        CAMPAIGNS["Campaign History"]
        MARKET["Market & Competitor"]
    end

    subgraph AI Workflow
        MATCH["Plan Matcher"]
        CONTEXT["Context Engine"]
        ELASTIC["Elasticity Model"]
        BUNDLE["Bundle Optimizer"]
    end

    BSS --> SUB
    BSS --> PLANS
    CDR_SYS --> USAGE
    CRM_SYS --> CAMPAIGNS
    CRM_SYS --> MARKET

    SUB --> MATCH
    PLANS --> MATCH
    USAGE --> MATCH

    USAGE --> CONTEXT
    CAMPAIGNS --> ELASTIC
    PLANS --> ELASTIC

    MATCH --> BUNDLE
    ELASTIC --> BUNDLE
Ontology Entity Source System Key Fields
Subscriber Profile BSS + CRM MSISDN, Segment, Tenure, Contract End, Device, ARPU, Payment History
Plan & Pricing BSS Product Catalog Plan ID, Name, Price, Data Cap, Voice Allowance, Validity, Add-ons
Usage Behavior CDR / Network Data Lake Daily Data (GB), Voice (min), SMS, Top Apps, Peak/Off-Peak Split, Roaming
Campaign History CRM Campaign Management Campaign, Offer, Channel, Response, Conversion, Date, Segment
Market & Competitor External Sources + CRM Competitor Plans, Pricing, Promotions, Market Share Estimates

AI Workflow

  1. Usage Profiling — Cluster subscribers into behavioral archetypes based on CDR patterns: data-heavy streamer, voice-centric, balanced user, international caller, weekend warrior, always-on IoT
  2. Plan Fit Analysis — For each subscriber, calculate: current plan utilization (% of allowance used per dimension), monthly overage charges, potential savings on alternative plans, and revenue impact to operator
  3. Contextual Trigger Detection — Monitor real-time events: data allowance approaching exhaustion, international roaming detected, contract within 30 days of expiry, birthday/anniversary, service complaint just resolved
  4. Offer Selection — When a trigger fires, select the highest-propensity offer from the eligible catalog based on the subscriber's archetype, price sensitivity, channel preference, and recency of last offer
  5. Elasticity Estimation — A/B test-driven measurement of price response per segment; build elasticity curves that predict volume impact of price changes within ±15% of current levels
  6. Bundle Optimization — Association rule mining on subscriber product holding patterns + CLV modeling to identify bundles that maximize 24-month subscriber value; test via limited market trials
  7. Competitive Response — Monitor competitor plan changes (price, allowance, promo); model subscriber migration risk; recommend defensive or offensive pricing actions
  8. Output — Personalized offer recommendations in app/web/SMS channels; pricing optimization dashboard for commercial team; bundle proposals for product management; competitive alerts for strategy

Dashboard & Alerts

Key Metrics

KPI Description Target
ARPU Average Revenue Per User per month 5-10% year-over-year growth
Offer Conversion Rate % of contextual offers accepted by subscriber > 18%
Plan Right-Sizing Rate % of subscribers on optimally matched plan > 65%
Bundle Penetration % of subscribers with 2+ products (plan + device/VAS) > 40%
Promotion ROI Incremental revenue / Promotion cost > 5x
Time to Launch (New Plan) Days from plan design to market availability < 5 days

Alert Rules

Alert Trigger Severity Action
Competitive threat Competitor launches plan with >15% better value-for-money in key segment High Model subscriber migration risk; fast-track pricing response
Offer fatigue detected Segment-level offer acceptance rate drops below 5% for 4 consecutive weeks Medium Pause campaigns for segment; refresh offer creative and targeting criteria
Data allowance trigger Subscriber reaches 90% of data allowance with >3 days remaining in cycle Medium Push contextual data booster offer via app notification
Contract expiry wave >10K subscribers in a segment entering 30-day contract expiry window Medium Launch targeted renewal campaign; personalize offers by churn risk
Price elasticity shift Measured elasticity for any plan changes >20% from previous quarter Info Recalibrate pricing model; assess if market dynamics have shifted

ROI Model

Metric Before After Impact
ARPU $42 $46 $4 increase → $240M incremental revenue (5M subscribers)
Contextual offer conversion 5% (batch campaigns) 18% (real-time contextual) 3.6x improvement
Overage revenue recovered as plan upgrades $2M (overage charges with churn risk) $8M (upgrade revenue with retention) $6M net improvement
Bundle penetration 22% 38% 73% increase → reduced churn + higher LTV
Plan launch cycle 4-6 weeks 3-5 days 90% faster time-to-market

Estimated Annual ROI

$10M - $25M annually from ARPU growth, improved offer conversion, bundle penetration, and faster commercial response — across a mid-size telco with 5M subscribers.

Implementation Notes

  • Personalized plan recommendations require individual-level CDR usage data mapped to current plan parameters from BSS
  • Contextual offer engine needs near-real-time data triggers (allowance thresholds, roaming events); integration with BSS policy/charging system and CRM campaign platform
  • Price elasticity measurement requires controlled A/B testing infrastructure; ensure statistical rigor (sample size, holdout groups, measurement windows)
  • Competitive pricing monitoring requires either automated web scraping of competitor websites or subscription to a competitive intelligence service
  • Dynamic plan configuration requires BSS product catalog flexibility; legacy BSS platforms may constrain the speed of plan changes

← Back to Catalogue | Previous: Network Capacity | Next: Field Operations →