Home›Telecom›AI for telecom›Product & Offer Personalisation← All AI guides
AI use case · Customer experience

Product & Offer Personalisation

Generic product recommendations shown to all subscribers have low conversion rates.

Recommendation EngineCollaborative FilteringBehavioural ML
MediumBusiness priority
Customer experienceDomain
5Main data sources

The problem

Generic product recommendations shown to all subscribers have low conversion rates. Subscribers receive irrelevant offer communications, reducing engagement and increasing opt-out rates.

The AI approach

Collaborative filtering and content-based recommendation models match subscribers to the most relevant offers based on usage patterns, spending behaviour, device type and subscription history. Real-time recommendations served at checkout, eCare and campaign touchpoints.

How it works

  1. 1
    Profile

    Build a profile of each customer's usage and preferences.

  2. 2
    Match

    Rank eligible offers from the catalog for that customer.

  3. 3
    Present

    Show the best offer in app, web, care or store.

  4. 4
    Learn

    Learn from what customers accept or ignore.

Data it uses

Product CatalogOrder HistoryBilling & Rating DataCRMCustomer Usage Data

How to measure value

Practical tips

Standards & references

Data governanceUse governed, consented data only
Responsible AIExplainable, monitored, human-in-the-loop

Related pages

This page describes generic industry practice and public standards. It is not based on, and does not describe, any particular vendor's product or operator's systems.