Torrent Gear

AI management of promotional campaigns and promotions

Principles of Responsible Promotion

1. Incrementality> coverage: the goal is an increase to the baseline, not a maximum of bonuses handed out.

2. Fairness and transparency: understandable conditions, the same rules for the same segments.

3. Data minimization: enough behavioral and product signals; PII - out of strict necessity.

4. RG/Ethics by default: Promo does not push risky behavior and respects player limits.

5. Without "twisting" chances: in gambling products, promos do not change RTP/probabilities, only the economy around (cashback, missions, etc.).

AI orchestration architecture promo

1) Data collection and normalization

Product events: sessions, deposits/purchases, missions, KYC/RG statuses.

Communication channels: in-app, e-mail, push, on-site banners.

Restrictions/policies: jurisdictions, limits, anti-fraud rules.

Hygiene: idempotency, timestamps, pseudonymization, TTL for raw data.

2) Models

Propensity/Next-best-action: probability of targeted action without promo.

Uplift/CATE: assessment of the incremental effect of a particular offer on a segment.

RNN/Transformer-Best send-time optimization.

Pacing/demand: forecast budget expenditure and audience saturation.

Anti-fraud promo: account/device graph, multi-pack and "split" payment schemes.

3) Orchestrator promo

Decides "to/what/when/where" in real time.

Complies with guardrails: frequency limits, cap on discounts, ban on offers "over" active RG limits.

Takes into account inventory/budget, conflict of resolution of offers and A/B split.

4) Causal evaluation and experiments

Holdout/geo-experiments and switchback designs.

Online evaluation of uplift (techniques T-learner/X-learner, doubly robust).

Reporting: incremental revenue, NMG (net marketing gain), LTV effect.

5) Observability and audit

Dashboards: pacing, contact frequency, response, ROI, anti-fraud incidents.

Decision logs: "to/what/why," model version, probability and expected uplift.

Transparency for the user: the center of promo history and conditions.

Promo formats (with AI feed)

Missions and progressions: Skill/time tasks (without affecting odds of winning). Rewards - cashback/skins/tournament ticket.

Cashback/rackback: dynamic bet on stability KPIs (e.g. lower on "race to lose").

Personal Offer Showcases: Content/Events/Seasons Relevant to the Player's Story.

Voluntary challenges: "slow mode "/" time-cap "for gentle play with soft rewards.

Surprise-and-joy: rare, fair gifts that don't depend on amounts.

Never: Do not offer offers that stimulate bypassing RG limits or increasing risks.

Anti-fraud and budget protection

Column of promo abuse: connections by devices/payments/behavior; identification of coupon "farms."

Cycling rules: limits on the number of activations/days/account/payment method.

Payment anomalies: monitoring returns/chargebacks after receiving a bonus.

CUS/geo-gardrails: offers are available only to relevant jurisdictions and statuses.

Confirmation threshold: large promos - after manual moderation or an additional verification step.

UX and Communications

Transparent conditions: a simple card "what, how much, before when, how to get."

Clear consequences: "bonus active for 7 days, no wagering required/rules X."

Neutral tone, no FOMO: No "urgent or miss the chance" pressure.

Promo Center: history, mission statuses, the ability to refuse communications.

Accessibility: large print, contrast, subtitles; localization of language/currency.

Success Metrics (KPIs)

Incrementality and economics

Uplift by target action/revenue, NMG (revenue − cost − promotional margin costs).

Cannibalization (% of actions that would happen without promo).

LTV effect and retention after the end of the promotion.

Operations

Budget pacing, contact frequency (per user), p95 offer delivery time.

Targeting/jurisdiction errors, channel timeouts.

Antifraud

TP/FP for promotional abuse, blocked amounts, average time to detection.

Repeated violations and rejected payments.

RG/Compliance

Offers stopped by RG gardrails, the share of players with active limits/pauses.

Complaints about incorrect conditions/pressure.

Trust/UX

CSAT/NPS on promo, CTR "terms details," unsubscribing from channels.

Algorithms in practice

Uplift-modeling

T-learner/X-learner on gradient boosts/tabular transformers.

Target - Δ between treated and control groups, regular recalibration.

Contextual Bandits (NBA)

Selection of the offer/channel/time for the context (device, hour, history, RG state).

Thompson Sampling/LinUCB with penalties for frequency and risks.

Pacing and budget

Forecast of daily demand and auto-distribution of limits (budget throttling).

Cap on offers in the cohort to avoid supply burnout.

Causal graphs and DR scores

Doubly-robust/IPS for online assessment when randomization is limited.

Graph adjustments for dependent users (referral effects).

Compliance and red lines

It is impossible: hidden conditions, offers that push you to bypass limits/self-exclusion, individual changes in chances/codes, manipulative texts.

It is necessary: log "why we show," audit of bias models, access to a person by controversial cases, quick cancellations in case of errors.

Roadmap 2025-2030

2025-2026 - Base

Data layer and promo orchestrator, frequency limits, holdout incrementality assessment.

Uplift V1 and channel/time bandit.

Anti-fraud promo: graph + cycling, promo center for the user.

2026-2027 - Maturity

Causal ML at the offer level, budget pacing with saturation forecast.

Multilingual communication, personal missions with RG-gardrails.

NMG/LTV reporting, automatic condition audit.

2027-2028 - Ecosystem

Marketplace of offers from partners (with uniform rules and audit).

It is a model device for private signals; explainability-cards "why you see it."

2028-2029 - Standards

General formats of logs/conditions, public reports on incrementality and ethics.

Extended causal experiments (switchback/geo) as normal.

2030 - Default

"Incrementality-by-design," certified gardrails, promo as a managed asset with understandable profitability and a minimum of risks.

Launch checklist (30-60 days)

1. Data and rules: connect product/channel events, set frequency limits and RG-gardrails.

2. Basic causality: include holdout and first 2-3 A/B on offers; measure uplift and NMG.

3. V1 models: propensity + uplift on boosts; channel/timing bandit.

4. Antifraud: cycling, graph of connections, manual moderation of large bonuses.

5. UX: promo center, transparent conditions, "refuse mailings" button.

6. Observability: dashboards pacing/ROI/abuse/RG; logs "to whom/what/why."

7. Processes: weekly calibrations, low uplift stock folding plan, quick cancellations on errors.

Mini-cases

Relouch players after the break: the uplift model shows that 5% cashback gives + 12% to return, and 10% - only + 2% from above and high abuse → leave 5%, limit the frequency.

Missions "slow mode": players with frequent long sessions - tasks with pauses and soft rewards; decrease in extra-long sessions by 19%, without falling LTV.

Anti-fraud coupons: the graph detects a "farm" of 31 accounts on one device → an autoblock of offers, a case for a review, a refund according to the policy.

AI makes promotional campaigns a managed asset, not a "discount lottery." Key ingredients for success:

causal incrementality assessment, RG/compliance and anti-fraud gardrails, transparent terms and conditions and respectful UX, budget pacing discipline and model auditing.

So promo really grows business, builds trust and supports healthy user behavior - without manipulation and gray areas.