Loyalty Systems Series

Applied loyalty architecture, simulations, and system design

This series collects my work on loyalty systems — where economics, governance, FX, partner rules, and user journeys intersect.
It includes writing, diagrams, and small technical projects that reveal how loyalty infrastructure actually behaves beneath the UI layer.

My goal is to make loyalty design legible — not as marketing, but as a distributed systems problem involving events, FX, tiering, reconciliation, and truth stability at scale.

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Purpose of This Series

Loyalty systems are often framed as marketing features.
In reality, they are:

• distributed economic engines
• deeply dependent on clean events
• tied to FX timing and regional rules
• sensitive to drift between services
• tightly coupled to financial liability
• reflections of a company’s truth architecture

Loyalty succeeds only when earning, FX, tiering, partner rules, and reconciliation stay aligned across markets and systems.

This series makes that architecture visible through essays, diagrams, and high-signal prototypes.

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Why This Matters for Product Strategy

Loyalty is one of the hardest system types to get right — and one of the easiest to get subtly wrong.

Clearer logic and stronger architecture lead directly to:

• consistent earning and redemption experiences across markets
• safer financial liability management
• fewer partner escalations
• stronger personalization and ML signals
• lower operational overhead from drift and reconciliation issues
• faster expansion into partners, campaigns, and new markets

These prototypes are not engineering tools — they are product clarity tools that help teams align on value, rules, and economics early in the lifecycle.

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Product Architecture Philosophy

Loyalty systems operate at the intersection of economics, truth, and experience.

My architecture philosophy is built on three principles:

1. Value must be consistent everywhere

Points, tiers, FX, and partner rules must converge on a coherent definition of value.

2. Rules define the real product

Earn logic, overrides, tier multipliers, and region-based conditions are the product surface.

3. Reconciliation is the source of truth

A loyalty platform only works when ledger, analytics, and user-visible balances all agree under pressure.

This series expresses that philosophy through interactive tools and clear system models.

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Writing

Essays exploring loyalty architecture, platform design, ML alignment, and data integrity.

• Why Loyalty Systems Are Some of the Hardest Products to Build (and the Most Underrated)
• The Platform Problem: Thinking in Boundaries When Shipping Features
• The ML Problem: Turning Data Into Decisions
• Designing for Data Integrity at Global Scale
• When Personalization Learns Too Fast

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Projects

Series Index

Prototype	Purpose	Live Demo	Repo
Loyalty Points Simulator	Simulate earning, FX normalization, partner rules, and tier multipliers	https://rtfenter.github.io/Loyalty-Points-Simulator/	https://github.com/rtfenter/Loyalty-Points-Simulator
Loyalty Drift Dashboard	Surface drift in targeting, promotions, and classification	https://rtfenter.github.io/Loyalty-Drift-Dashboard/	https://github.com/rtfenter/Loyalty-Drift-Dashboard
Loyalty Event Contract Validator	Validate loyalty event rules, naming, types, and schema drift	https://rtfenter.github.io/Loyalty-Event-Contract-Validator/	https://github.com/rtfenter/Loyalty-Event-Contract-Validator
Tier Progression Visualizer	Earn → FX → tier → partner multipliers	https://rtfenter.github.io/Loyalty-Tier-Progression-Visualizer/	https://github.com/rtfenter/Loyalty-Tier-Progression-Visualizer
Partner Rule Tester — Loyalty Edition	Region eligibility, overrides, partner exceptions	https://rtfenter.github.io/Loyalty-Partner-Rule-Tester/	https://github.com/rtfenter/Loyalty-Partner-Rule-Tester
Redemption Value Integrity Checker	Value parity across markets and partners	https://rtfenter.github.io/Loyalty-Redemption-Value-Checker/	https://github.com/rtfenter/Loyalty-Redemption-Value-Checker
FX Drift Analyzer for Loyalty Value	FX shifts → value distortion → fairness issues	https://rtfenter.github.io/Loyalty-FX-Drift-Analyzer/	https://github.com/rtfenter/Loyalty-FX-Drift-Analyzer
Loyalty Ledger Reconciliation Sandbox	Earned vs redeemed vs expired vs corrected	https://rtfenter.github.io/Loyalty-Ledger-Reconciliation-Sandbox/	https://github.com/rtfenter/Loyalty-Ledger-Reconciliation-Sandbox

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System Diagrams

Earn → Ledger → Redeem Event Flow

    [User Purchase]
          |
          v
    [Earn Event Created]
          |
          v
    [Event Ingest Layer]
          |
          v
    [Points Engine]
      - apply FX (normalize)
      - apply earn rules (rate, tier, partner)
          |
          v
    [Points Ledger]
      - record points_earned
      - update balance
          |
          v
    [Redemption Triggered]
          |
          v
    [Redeem Event Created]
          |
          v
    [Redemption Engine]
      - validate balance
      - apply reward rules
          |
          v
    [Liability & Reporting]

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Points Truth Drift Map

                [Source of Truth: Points Ledger]
                           |
           -----------------------------------------
           |                   |                  |
           v                   v                  v
   [Service A]           [Service B]        [Service C]

Examples:
- Service A: value = $0.01
- Service B: outdated config
- Service C: missing partner rules

Result:
- inconsistent balances
- mismatched liability
- ML trained on contradictory truth

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FX Conversion and Tiered Value Reconciliation

[Purchase: amount + currency + partner + tier]
                    |
                    v
           [FX Normalization Layer]
                    |
                    v
         [Tier & Rule Evaluation Engine]
                    |
                    v
             [Points Calculation]
                    |
                    v
            [Ledger & Liability View]

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Partner-Specific Rules & Multi-Market Drift

               [Global Program Config]
                         |
        -----------------------------------------------
        |                     |                       |
        v                     v                       v
   [Partner A]           [Partner B]              [Partner C]

Potential Drift:
- inconsistent earn logic
- FX timing mismatch
- stale overrides
- partial rollouts

Outcome:
- multiple “truths”
- reconciliation pain
- unfair value distribution

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Portfolio & Writing

• Medium: https://medium.com/@rtfenter
• LinkedIn: https://www.linkedin.com/in/rtfenter/
• GitHub: https://github.com/rtfenter

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About This Repo

This repository is the central hub for all loyalty-related work — writing, diagrams, prototypes, and system models.

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Technologies Used

These prototypes are intentionally lightweight:

• HTML / CSS / JavaScript
• GitHub Pages hosting
• No backend required

The goal is clarity: high-signal tools that communicate loyalty logic without infrastructure overhead.