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 | Hospitality & services |
|---|---|
| Platforms | AEP, Journey Optimizer, CJA, Web SDK |
| Type | Academy project case study (simulated environment, synthetic data) |
| Roles practised | XDM modeller, AJO developer, CJA analyst, QA specialist |
| Environment | aramark.infiante360techacademy.com |
The business challenge
A hospitality and services business runs loyalty, service requests, product views and offers across many locations. Offer performance cannot be attributed, service follow-ups are manual and analytics teams spend days reconciling data before they can answer simple questions.
Target architecture
Implementation approach
- ERD & use cases — entities and journeys
- Schemas — profile, events, lookup
- Ingestion — CSV via workflows
- Attribution IDs — offer and journey IDs
- Journeys — loyalty + service
- CJA reporting — ops and offers
Use cases implemented
- Loyalty tier-upgrade journey
- Service-request follow-up and satisfaction survey
- Product-view and call-to-action event tracking
- Offer attribution with journey and offer identifiers
- Journey summary reporting for operations
- Win-back audience for lapsed members
Validation and governance checks
- Experience Event datasets use the standard _id, timestamp and eventType fields rather than custom duplicates
- Every schema field expected by a dataset exists before ingestion; mismatches are logged in the backlog
- CSV data ingested through workflows; JSON or Parquet for direct upload
- Attribution identifiers mapped consistently across journey, offer and customer datasets
- CJA data view components named and documented for analysts
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 — XDM modeller, AJO developer, CJA analyst, QA specialist — 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
Why do Experience Event datasets fail on missing IDs?
Each record needs a unique _id. In the project we compute a UUID during mapping rather than relying on source files to supply one.
Is this a real company's data?
No. The simulator uses synthetic data and implies no affiliation with any company.
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