Why it is important to segment the audience of players

Introduction: one message does not work

Different players have different motives, budgets, rhythms and risks. Universal campaigns "for all" are expensive and blind: part of the audience is burned out, part is ignored, and risky cohorts receive unnecessary triggers. Segmentation makes marketing, product and support accurate: you say "the right things - the right people - at the right moment," increase LTV and reduce harm.

1) What does segmentation give a business (and a player)

More value for the same money. Personal onboarding and offers reduce payback and increase ARPPU.

Reduced outflow. Different reasons for leaving → different actions: somewhere you need a demo training script, somewhere - a quick CUS/payments.

Honest UX. The player sees relevant prompts (RTP, limits, help), not spam promos.

Responsible Gaming. Risk segments are automatically excluded from the "heating," receive pauses and help.

Operational clarity. The team understands for whom it builds features and content - the speed and quality of solutions are growing.

2) Basic axes of segmentation: from which we "collect" the portrait

1. Value: RFM/income/frequency/rate volatility → L/H/M cohort.

2. Behavior: deposit frequency, IAT, night activity, withdrawal cancellations, favorite games/markets.

3. Lifecycle: New (D0-D7 )/Grow (D8-D30 )/Steady (D31 + )/Churn-risk/Re-activated.

4. Risk (RG): low/medium/high in rule flags and ML scoring (chasing, night binges, re-deposit loops).

5. Motif/Psychography: Learning/Entertainment/Socialization/Competition/Hyroll (by survey and behavioral proxy).

6. Geo and payments: jurisdiction, language, payment methods, KYC/SoF claims.

💡 Important: sensitive signs (age <18/21, health, religion, politics, etc.) - never used for targeting. Age filters are only compliance barriers.

3) Model set: how to "count" segments

RFM and fast start threshold rules.

Clustering (k-means/DBSCAN) by frequency, amounts, circadian features, volatility - for behavioral archetypes.

Propensity models (logreg/GBM) for events: deposit, outflow, response to training letter.

Uplift models - where communication really changes behavior, and not just gets "active."

RG scoring (risk outcomes): the probability of self-exclusion/crisis is → used only to limit promo and help.

Change-point detection - catches sudden shifts (escalation of the night, bursts of deposits).

Technical principles: temporal validation (without leaks), calibration, fairness-checks cohort-by-cohort.

4) Segment map and what to do with them (example)

Segment	Key features	What we show	What we don't do
New Learners (D0–D7)	Low frequency, demo, FAQ	Onboarding steps, demos, RTP/limit cards, fast KYC	Aggressive bonuses "urgently"
Value Grow	Stable deposits, interest in 2-3 games	Personal guides, tours of new modes, transparent T&C	Pressure on rates/limits
Steady Casuals	Weekend play, low volatility	"No pressure" reminders, bankroll content	Frequent fluffs at night
VIP/SoF verified	High amounts, confirmed funds	Premium service, fast payouts, hard RG triggers	RG Bypass Permissions
Churn-risk	Frequency drop, negative tickets	Service solutions, "fixed - that's how," honest update	We will live with a "bonus at any cost"
RG-risk (L3–L4)	Night, re-deposit loops, output cancellations	Pause/limits/help, minus promo, training	Any reactivations and promos

5) Activation: where to use segments

In product

Onboarding by role: newcomers see demos and rules; experienced - fast start, but with visible limits.

Widgets by context: long session → timeout; win → fix 50-80% and "do not cancel withdrawal."

Money Status Center: Transparent payouts/LCCs for segments with low waiting tolerance.

IN CRM

D0-D7 series: training, limits, Reality Check → not promo spam.

Re-generation: only for low-risk, with service value (new payment methods, localization, fast KYC).

RG filters: exclude risk segments from reactivations; give help and pause.

In media

Brand vs Performance by Market/Value Segment.

Affiliates 2. 0: quality-score on retention/LTV and RG complaints.

6) Metrics: How to know segmentation works

Business: LTV↑, Payback↓, ARPPU/ARPDAU↑, share of repeated depozitov↑, CAC is stable/lower in "clean" channels.

Behavior: D7/D30/D90 Retention↑, Time-to-1st- value↓, less tickets "where is my conclusion."

RG/ethics: the share of players with active limitami↑, nightly "zapoi"↓, cancellations, vyvodov↓ 0 reactivations in risk segments.

Communications: OR/CTR↑, otpiski↓, zhaloby↓, CSAT/NPS↑.

Models: PR-AUC/calibration is normal, fairness gaps do not grow.

7) Data, privacy and compliance

Minimization: we use aggregates and behavioral windows (7-30 days), without excessive personal details.

Transparent consent: goals - service, security, Responsible Gaming.

RBAC and audits: role access, activity logs, retention periods.

Bans: no targeting of minors/vulnerable; there is no export of RG signals to aggressive marketing.

8) Frequent mistakes (and how smart)

"Cut 40 segments - no resources." → 6-10 working segments, then deepen.

Retarget all in a row. → RG filters and "minus promo" for risk - iron.

Click to score. → Measure retention/LTV and increment (geo-holdout/MMM).

Static. → Recalculation of segments 1-4 times a month; change-point catches drift.

"Black box" models. → SHAP/feature imports, human explanations in the player card.

9) Implementation Roadmap (0-30-90 days)

0-30 days - foundation

Collection of basic events (deposits, conclusions, duration, IAT, night share), CCM/payout card.

RFM + Lifecycle v1, RG-rule-flags.

CRM rules: exclude risk segments; D0-D7 - Training/Limits

31-60 days - deepening

Behavioral clustering (k-means), segment cards.

Propensity for outflow/learning response.

Product widgets by context (long session → pause; winning → fixing).

61-90 days - scale and control

CRM uplift tests, geo-holdout for media.

Single dashboard: Growth + RG + Service.

Fairness audit and segment recalculation procedures.

10) Checklists

Data/Models

RFM and Lifecycle are calculated
Behavior clusters and RG scoring
Propensity and uplift to key scenarios
Temporal validation, calibration, fairness

Product/CRM

Onboarding Series D0-D7 (Training, Limits)
Triggers: Long Session/Win/Cancel Output
Exclusion of risk segments from promo
Visible "control center" (limits/pause/statuses)

Measurement/Ethics

Retention/LTV by Segment and Channel
RG metrics and dashboard complaints
Geo-holdout/MMM for increment
Data policies, RBAC, retention

11) Examples of correct messages by segment

To newcomers: "Welcome! Demo and rules in 1 click. Limits and timeout - in the profile. 18+/21+.»

Steady Casuals: "Planning an evening? Include a time reminder. Bankroll Guide - Inside"

After winning: "Congratulations! Fix 50-80% of the lead. Changes - only tomorrow after sleep"

Risk segment: "We noticed an intense game at night. We recommend a pause/limit. Help is available 24/7"

Segmentation is a lever of precision: you save budget, speed up product decisions, build trust and reduce risk. Start with RFM + Lifecycle and RG filters, add behavioral clusters and propensity/uplift - and activate segments where it really helps the player: in onboarding, payments, self-control tips and respectful communications. So the brand gets not just traffic, but a stable base of players who stay for a long time - and remain safe.