Casino CRM stack: segmentation, campaigns, personalization

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💡 18+. Material for iGaming platforms/operators and marketing/analytics teams. Not a call to play. All examples are about corporate architecture and secure communication with users.

1) CRM objectives in iGaming

LTV growth and retention: return the player in time with a suitable channel and offer.

Reducing the cost of communications: smart channel/time/frequency selection.

Compliance by default: RG/AML/opt-in, age/geo-restrictions, bans on promotions for vulnerable groups.

Transparent attribution: understand what really works.

2) CRM stack reference architecture


Events (PAM/Wallet/RGS/Payments/Web/App)
│
├─CDP (Identity + Profiles + Consent) ──Feature Store (real-time + batch)
│         │
│         ├─Segmentation Service (rules, SQL, ML lists)
│         └─Orchestrator (Journeys/Triggers/Limits)
│                  │
│                  ├─Channels: Push / Email / SMS / On-site / In-app / Call
│                  └─Offers Engine (bonuses, missions, jackpots)
└─BI/DWH (attribution, uplift, experiments)

Key elements:

CDP (Customer Data Platform) with player profile and permissions (consent).
Orchestrator scripts/campaigns with frequency limits and RG rules.
Feature Store for online/batch characteristics (RTP propensity, favorite providers, risk).
Offers Engine - generation and execution of offers (rules + ML).
Channels with uniform contracts and feedback (delivery/open/click/reply/spam).

3) Event model and player profile

3. 1 Baseline events

`session. started/ended`

`bet. placed/settled` (stake/win/in_bonus/provider/game)
`wallet. debit/credit` (reason, latency)

`payment. intent	authorized	captured	refund	withdraw`
`bonus. issued	consumed	expired`
`rg. limit. hit	reality_check`
`crm. delivery	open	click	unsubscribe	complaint`

3. 2 Profile (fragment)

json
{
"player_id":"p_123",  "brand_id":"A",  "region":"EU",  "locale":"de-DE",  "rg_status":{"self_excluded":false,"limits":{"loss_daily":100}},  "consents":{"email":true,"push":true,"sms":false,"profiling":true},  "features":{
"tenure_days":186,   "dep_count_30d":3,   "churn_score":0. 62,   "fav_providers":["studio_x","live_y"]
},  "last_seen_at":"2025-10-22T21:10:00Z"
}

Rules: all PII - tokenize; keep the sign of consent and the date of change. Any communication - only with an active opt-in.

4) Segmentation: rules + ML

4. 1 Rules (rule-based)

SQL/visual constructors: "DE + dep_count_30d=0 + last_seen>7d + consent. email».

Segment directories (VIP, beginners, high-value, dormant).

Update: real-time (stream) for critical triggers, batch (5-60 min) for wide campaigns.

4. 2 ML lists

Churn propensity, Next Best Action/Game, Deposit intent, Offer sensitivity.

DWH training, scoring in the Feature Store; explainability: top signs, trust.

5) Offers and personalization

5. 1 Types of offers

Bonuses (deposit/cashback/free spins), missions/quests, tournaments, jackpot prizes, personal recommendations of games/categories.

5. 2 Compatibility rules

RG: exclude self-excluded/limit; age/license/region.

Economy: max cost per player/day, vager/max bet, conflict block.

Anti-spam: frequency per channel and per player.

5. 3 Generating an offer (example API)


POST /v1/offers/generate
{
"player_id":"p_123",  "context":{"intent":"reengage","channel":"email"},  "constraints":{"max_cost_minor":500,"rg_safe":true}
}
→ 200 {
"offer_id":"of_777",  "template":"bonus_cashback",  "params":{"percent":10,"cap_minor":2000,"wagerx":15},  "expires_at":"2025-10-24T21:00:00Z"
}

6) Orchestration of campaigns and triggers

6. 1 Triggers (real-time)

`bet. settled 'with a non-trivial loss → "consolation" cashback (if RG allows).

`payment. failed '(3-DS/AVS) → prompt/alternate PSP.

`churn_score>0. 7 & last_seen>14d' → re-engage chain (push→email).

6. 2 Jorney (journeys)

State graph: enter → wait → check → send → evaluate → next step.

Entry/exit conditions, deaddown by player, cooldown between steps, automatic opt-out on unsubscribe/complaint.

6. 3 Frequency limits and priorities

Per channel/day/week, global "message cap," VIP/incident priority.

"Four-eyes" on sensitive campaigns (manual approval of high-denomination offers).

7) Channels and delivery

Channel	When to use	Features
Push	fast trigger, mobile expectations	quiet hours, deep-link, permian
Email	complex offers/content	DMARC/SPF/DKIM, warm-up, IP segregation
SMS	critical fast	expensive, short, legal restrictions
In-app/On-site	context in product	content recommendations, personal banners
Call/Support	VIP/Risk Cases	call log and limits

Deliverability: domains, IP reputation, warm-up, spam triggers; tracking 'delivery/open/click/unsubscribe/complaint'.

8) Personalize content and recommendations

Rules + ML hybrid: first filters by license/provider, then ML ranking (history-based + popularity/novelty).

Context: device/time/geo/category.

Guardrails: exclude "dangerous" RG patterns (long sessions/high stakes), limit on bonus restrictions.

Templates: multilingual content (BCP-47), placeholders for offer variables, A/B options.

9) Experimentation and attribution

A/B/n with persistent bucketing.

Uplift modeling: we target those who expect an increase from contact (and not all).

Attribution: last-touch + positional models; for triggers - "saw/opened/clicked → action (deposit/return/involvement)."

Guardrails: do not worsen RG-indicators (increase in triggering limits, complaints).

10) CRM Metrics and SLO

Delivery: delivery rate, open/click, complaint/unsubscribe.

Business: uplift deposits/reactivations, ARPU uplift, churn-down, ROI campaigns, cost per engaged.

Operations: offer generation time, p95 "sobytiye→otpravka," message queue, retrays.

RG/Compliance:% blocked by RG, share of contacts with vulnerable, complaints.

SLO objectives (benchmarks):

real-time trigger "sobytiye→dostavka" p95 ≤ 30-90 s;
batch campaign up to 15 min;
complaint rate < 0. 1%, unsubscribe <1% by mailing.

11) Security, privacy, consent

Consents are versioned; for each communication we log "on what basis we sent."

PII isolation: tokens/pseudo-ID in CRM, direct contacts in secure channel repositories.

RLS/ABAC: access by brand/region/role (support/marketing/analytics).

WORM audit: changes to segments, rules, offers, mass mailings.

Displacement by region (data residency), "right to oblivion."

12) Integration contracts (fragments)

Trigger Event


POST /v1/events
{
"event_type":"payment. failed",  "trace_id":"tr_a1b2",  "player_id":"p_123",  "payload":{"psp":"X","reason_code":"3DS_TIMEOUT"},  "occurred_at":"2025-10-23T11:21:05Z"
}

Send Message (Abstract Channel)


POST /v1/messaging/send
Headers: X-Idempotency-Key: msg_001
{
"channel":"email",  "player_id":"p_123",  "template_id":"tpl_reengage_01",  "personalization":{"first_name":"Alex","offer_id":"of_777"},  "frequency_policy_id":"fp_default"
}
→ 202 {"delivery_id":"dlv_9k","status":"QUEUED"}

Feedback from the channel


POST /v1/messaging/feedback
{
"delivery_id":"dlv_9k",  "event":"open    click    bounce    complaint    unsubscribe",  "occurred_at":"2025-10-23T11:22:05Z"
}

13) Operational hygiene

Campaign calendar: black windows (matches, releases, reg periods), "quiet hours."

Content Review: Spelling, Legal Disclaimer, Brand Fit and License.

Deadup: Don't send two messages about the same event for X minutes.

Back-pressure: limit peak mailings, warm up domains, prioritize transaction messages.

14) Checklists

Architecture and data

Single CDP, profiles, consents, RG statuses.
Stream of events and batch funnel; Feature Store real-time + batch.
Outbox/CDC, idempotent send and feedback loop.
RLS/ABAC, PII isolation, WORM audit.

Segmentation and offers

Set of "skeleton" segments + ML-lists.
Interoperability policies (RG, economics, licenses).
Frequency limits per channel and globally.

Orchestration and channels

Jorney with cooldown and unsubscribe/complaint auto-out.
Channel monitoring deliverability, domain reputation/IP.
Deep-link tracking and conversion to wallet/bet.

Experiments/Measurement

A/B/n + uplift; guardrails RG.
Attribution and ROI, cost report (channel/PSP/locale).

15) Red flags (anti-patterns)

Mass mailings without frequency limits and RG filters.

Campaigns on players without opt-in or with expired consent.

Personalization using PII in plaintext unnecessarily.

No feedback loop: no delivery/complaint data.

"Hard-wired" rules without A/B and telemetry.

Sending bonuses without economic control (cap, budget, rule conflict).

Storage of contact data in logs/dashboards.

16) The bottom line

The strong CRM stack in iGaming is not only "mailing." It is an event platform with a single profile, consents, and RG constraints; smart segmentation and generation of offers; orchestrator of the journal with frequency limits and channel feedback; and measuring uplift/ROI instead of "discovery for discovery's sake." So you increase LTV and retention, reduce contact costs, comply - and make communications appropriate, timely and safe.