Optimization of affiliate relations

1) Why optimize connections

• Improves user quality (Retention/LTV) by reducing CAC/Payback
• eliminates arbitration and attribution duplicates;
• makes payments predictable and fair;
• accelerates scaling in new regions/chains.

2) Roles and responsibilities

Affiliate/Publisher/Influencer: traffic delivery, compliance with creative and compliance policies.
Operator/Platform: conversion funnel, anti-fraud, billing/payments, reporting.
Aggregator/Network: intermediary facilitating MTA/end-to-end ID.
Content/Studio: retention mechanics, joint promo/prize pools.
Compliance Gate: age/geo/sanctions, advertising restrictions.
Auditor/Regulator: external oversight, disputes and sanctions.
Treasury/治理: payment parameters, RNFT templates, sunset edits.

3) Communication architecture and identification

DID + ULID/trace-id - single end-to-end vizit→sessiya→sobytiya→vyruchka correlation.
Click-id/Sub-id/Deep-link: multi-level source and creative label.
RNFT passport of the partnership: rights/limits/payment model/vetting/fines.
Attribution Hub: collecting signals (clicks, postbacks, server events), dedup, attribution windows.

4) Attribution (MTA) and micro-contribution

Models: last-touch, position-based, time-decay, data-driven (Shapli/Markov).

• Apply hybrid: data-driven for budget allocation + simple reporting model for payout transparency
• fix windows: click-window (for example, 7-30 days), view-window (1-3 days) by verticals;
• Consider post-install contribution (D1/D7 activity, first valuable action)
• store micro-contribution (channel shares) for subsequent rate optimization.

5) Economic models and pricing

CPL/CPA/RevShare/Hybrid: selection by product stage and risk.
Cliff/vesting: deferral of payments until quality verification (chargeback period).
QF (Quality Factor): multiplier to be paid after quality (Retention, ARPPU, disputed leads).
Dynamic Payout/Bid Shading: Automatic rate cuts/increases on predicted LTV and fraud risk.
Frequency caps/dedublication: protection against spam and "split" leads.

[
\text{Payout}=\text{CPA}\cdot I_{\text{qual}} + \text{RevShare}\cdot \text{NetRev}\cdot QF - \text{Adjustments}_{\text{fraud/chargeback}}
]

6) Traffic routing (quality & cost-aware)

Utility(offer    route) =
wQ·Predicted_Quality(LTV,Risk) - wC·Cost_per_acq
+ wP·Propensity_to_Convert + wG·Geo/PolicyScore

Quality/LTV - from models (see § 10); Risk - anti-fraud/compliance;

Cost_per_acq - actual CAC taking into account media databases;

Propensity_to_Convert - probabilistic assessment of conversion on the current landing.

Traffic goes to options with maximum Utility when performing invariants: compliance, RNFT limits, frequency limits, budget/diary.

7) Funnel optimization (CRO/personalization)

Segmentation: geo, device, channel, intention stage, source of creativity.
A/B/n: landing pages, onboarding, KYC forms, pay methods, first missions/promos.
Latency Mesh-hook: fast delivery of resources for cold traffic.
Content localization: language, payments, cultural triggers.
Incentive harmonization: Promotional missions/battle passes that do not create a long-term imbalance.
Interface with RNFT rights: individual limits/quotas/access to offers by affiliate quality.

8) Anti-fraud and anti-arb

• Cookie-stuffing/fingerprinting: server attribution, event signatures, one-time tokens.
• Insentived fraud/bot farms: control tasks/behavioral signatures, device graph, ML filters.
• Coupon-arb/code-sharing: one-time vouchers, linked to DID, TTL.
• Registration pharming/chargeback: cliff/vesting, risk scoring, escrow payments.
• Gray geo-brokerage: ZK-geo-proofs, geo-anti-evasion signatures, quarantine.

9) Privacy and compliance

DID/VC: auditable credits, minimum personal data from affiliates.
ZK proofs: age/geo/statuses without disclosure.
Selective telemetry: aggregates/embeddings instead of "raw" logs.
Advertising and disclosures: creative templates, stop words, media whitelisting.
Taxes/Deductions: Automation on the payout path through Rewards Router.

10) Quality Forecast: LTV/Retention/Risk

LTV/ARPPU models: gradient boosting/GLM + censoring adjustment.
Early-signals: onboarding depth, first payments, session frequency, cohorts.
Risk scoring: anomalies, matches of prints, correlations with "dirty" segments.
Calibration: Platt/Isotonic; periodic PSI/JS drift control.
Uplift models: who should stimulate promo, and who will convert anyway.

11) Experiments and budget policies

A/B/n: offers/bets/creatives/landings/payments.
Bandits/Thompson: adaptive traffic distribution with soft limiters.
Budget throttling: closing "minus" offers, auto-turn in plus.
Holdout cohorts: measurement of true incremental effect.
Sunset windows: temporary changes in rates/weights with auto-rollback.

12) RNFT contracts for affiliates

• 'model ': CPA/CPL/RevShare/Hybrid,' windows': click/view, 'caps': day/week.
• 'quality ': QF curve, controversial leads, cliff/vesting, chargeback rules.
• 'compliance ': regions/channels, creative policies, audits, fines.
• 'economics': rates/corridors, bonus triggers, escrow.
• 'governance ': versions of scales, sunset edits, right of appeal (Dispute/Escrow).
• 'S-stake ': pledges for high-risk, slashing terms.

13) Observability and reporting

End-to-end tracing: 'click _ id', 'aff _ id', 'campaign _ id', 'rnft _ id'.
Метрики: CTR/CR, D1/D7, ARPPU, NRR/GRR, CAC/Payback, chargeback%, fraud-rate.
Панели: Affiliate Quality, LTV/Uplift, Attribution & Dedup, Risk & Disputes, Promo Performance.
SLO: delay of postbacks, payout accrual time, MTA accuracy.

14) Formulas and landmarks

Incremental CR = CR(test) − CR(holdout)

Payback (дней) = CAC / (Avg Daily Gross Margin per user)

QF = f(retention, ARPPU, dispute/1k, chargeback%)

Uplift ROI = (ΔNetRev − ΔCost)/ΔCost

Fairness Index (Jain) on budget distribution between affiliates

CAC = Spend / Qualified Actions

• Dispute ≤ 2-3% of qualified actions; chargeback ≤ 1–2%.
• D7 retention ≥ target threshold by market; Payback ≤ 90 days (B2C).

15) 治理 (weights, limits, prices)

Proposals: changing rates/corridors/QF and attribution windows.
R-weighting of votes: high-quality participants receive ↑ves.
Public Reports: Quarterly Quality Metrics by Affiliate.
Black/gray lists: tactics and sources, signatures of violations.
Sunset clauses: temporary changes with auto-rollback.

16) Implementation playbook (in steps)

1. Channel/geo/creative mapping, auditing of current attribution and fraud.
2. Unified identification: DID + ULID, server postbacks, dedup.
3. RNFT 1. 0: contract templates, caps/windows/cliff/escrow/fines.
4. MTA & qualities: run hybrid attribution, QF-multiplier.
5. LTV/Risk models: forecast/calibration, connect to budget routing.
6. Anti-fraud: signatures, control tasks, quarantine of controversial leads.
7. CRO: A/B landing, localization, payment methods, missions.
8. Bandits: adaptive traffic delivery, fairness/budget limiters.
9. Observability: quality/risk/economics panels; SLO postbacks/payouts.
10. Pilot 1-2 quarters: retro-calibration of rates/windows/QF; public report.
11. Scaling: new geo/chains/partners, pricing automation.

17) KPI of the optimization program

Economy: CAC↓, Payback↓, NRR/GRR↑, share of re- vyruchki↑.
Quality: D1/D7/D30↑, ARPPU/LTV↑ by segment/channel.
Risk: dispute/chargeback↓, proportion of quarantine lidov↓, sporov↓ parsing time.
Operations: postbekov↓ delay, MTA↑ accuracy, vyplat↑ stability.
Fairness: Jain-index of the budget in the corridor, a decrease in the concentration of risky sources.
Growth: Scale in new geo/chains without quality degradation.

18) Delivery checklist

• Single ID Loop (DID/ULID), Server Attribution and Dedup
• RNFT templates with caps/cliff/escrow/penalties and QF curve
• Hybrid MTA started and attribution windows defined
• LTV/risk models are connected to routing and rates
• Anti-Fraud Signatures, Quarantine, Control Jobs
• A/B/n and bandits for offers/landings/creatives
• Dashboards of quality/risk/economics and SLO postbacks/payouts
• Pilot Passed, Recalibration and Public Report
• 治理 - balance/window/price procedures with sunset
• Geo/Chain and Partner Scaling Plan

19) Glossary

MTA: multi-touch attribution.
QF: multiplier of payments by quality.
RNFT: contract of relations/rights/limits and KPI.
ULID/trace-id - end-to-end event ID.
CAC/Payback/LTV/NRR: Key Traffic Economics Metrics.
Bandits: adaptive distribution of traffic between variants.

20) The bottom line

Optimization of affiliate ties is a single contour of data, economics and compliance. End-to-end attribution, RNFT contracts, quality forecasting and anti-fraud, combined with budget routing and A/B experiments, turn the affiliate network into a managed "growth engine": traffic is directed to where it gives maximum LTV with minimal risk and cost, and partners receive transparent and fair conditions.