Case study at a glance
About this case study: this is an Infinite360 Tech Academy project case study delivered on a simulation environment with synthetic data. It shows the scope, architecture and approach our consultants and learners practise. It is not a client engagement and implies no affiliation with, or endorsement by, any company or brand.
| Industry | Telecom |
|---|---|
| Platforms | AEP, Real-Time CDP, Journey Optimizer, CJA, Query Service |
| Type | Academy project case study (simulated environment, synthetic data) |
| Roles practised | Data engineer, data scientist, AJO developer, CJA analyst |
| Environment | tele.infiante360techacademy.com |
The business challenge
A telecom operator sees churn only after a subscriber ports out. Usage drops, repeated network issues and contract end dates live in separate systems, so retention teams cannot intervene early and offers are not matched to the reason a subscriber is at risk.
Target architecture
Implementation approach
- Define churn signals — usage, issues, contract
- Model & ingest — subscriber + events
- Score risk — Query Service / model
- Audiences — risk tiers
- Journeys — save, upgrade, apology
- Measure — holdout comparison
Use cases implemented
- Churn-risk audiences by reason (price, network, contract end)
- Network-incident apology journey with service credit offer
- Contract-end upgrade journey
- New-subscriber onboarding and activation
- Paid-media suppression for recently saved subscribers
- Retention measurement with a holdout group
Validation and governance checks
- Risk features engineered outside the profile and ingested as clean attributes
- Holdout group defined before launch so impact can be measured honestly
- Network-event journeys throttled to avoid message floods during outages
- Household and individual identities kept distinct to prevent false merges
- Offer eligibility and fallbacks tested for every risk tier
What Infinite360 brings
Architecture first
A documented target architecture, identity strategy and data model before a single journey is built.
Reusable accelerators
Schema patterns, naming conventions, backlog templates, QA checklists and runbooks reused across projects.
Validation discipline
Every stage is validated - payloads, identity graphs, profile fragments, audience counts and journey test runs.
Governance built in
Consent, data usage labels and least-privilege access are designed in, not bolted on at go-live.
Knowledge transfer
Walkthrough recordings, documentation and pairing so your team can run and extend the solution.
Day-one-ready people
Consultants and contract engineers who have practised the same pattern end to end before joining your team.
From case study to contract delivery
The roles that practise this case study — Data engineer, data scientist, AJO developer, CJA analyst — are the same profiles we place through contract hiring and staff augmentation. Because they have already built this pattern end to end, onboarding focuses on your systems and standards rather than on learning the platform.
Frequently asked questions
How is churn risk calculated?
In the project, features such as usage decline, repeated incidents and contract end date are combined in Query Service or an external model and ingested as profile attributes.
Is this a real operator's data?
No. All subscriber data in the simulator is synthetic.
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