AI-based CRM analytics system for iGaming
iGaming CRM is player base management based on behavior segmentation, churn prediction, and bonus campaign optimization. The main goal is to retain profitable players without incentivizing problematic behavior and to cost-effectively reactivate the dormant segment.
Segmentation and Value of Players
import pandas as pd
import numpy as np
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.cluster import KMeans
class iGamingPlayerSegmentor:
"""Сегментация по GGR и поведению"""
def compute_player_value(self, player_data: pd.DataFrame) -> pd.DataFrame:
"""
GGR (Gross Gaming Revenue) и LTV расчёт.
GGR = deposits - withdrawals - bonuses_paid
"""
df = player_data.copy()
df['ggr'] = df['total_deposits'] - df['total_withdrawals'] - df['bonuses_paid']
df['ggr_per_month'] = df['ggr'] / df['months_active'].clip(1)
df['deposit_frequency'] = df['deposit_count'] / df['months_active'].clip(1)
df['withdrawal_ratio'] = df['total_withdrawals'] / df['total_deposits'].clip(1)
# LTV прогноз: простая линейная проекция
df['predicted_ltv_12m'] = df['ggr_per_month'] * 12 * (
1 - df['churn_probability_30d'].fillna(0.3)
)
return df
def segment_players(self, player_value: pd.DataFrame) -> pd.DataFrame:
"""Сегментация по value и активности"""
df = player_value.copy()
ggr_median = df['ggr'].median()
activity_median = df['sessions_last_30d'].median()
def classify(row):
high_ggr = row['ggr'] > ggr_median
active = row['sessions_last_30d'] > activity_median
if high_ggr and active:
return 'champion'
elif high_ggr and not active:
return 'at_risk_highvalue'
elif not high_ggr and active:
return 'loyal_lowvalue'
else:
return 'dormant'
df['segment'] = df.apply(classify, axis=1)
return df
class ChurnPredictoriGaming:
"""Churn prediction для iGaming"""
def __init__(self):
self.model = GradientBoostingClassifier(
n_estimators=200, learning_rate=0.05, max_depth=4, random_state=42
)
def build_features(self, players: pd.DataFrame) -> pd.DataFrame:
return pd.DataFrame({
'days_since_last_session': players['days_since_last_session'],
'sessions_trend': (players['sessions_last_7d'] - players['sessions_prev_7d']) / (players['sessions_prev_7d'] + 1),
'ggr_trend': (players['ggr_last_30d'] - players['ggr_prev_30d']) / (abs(players['ggr_prev_30d']) + 1),
'deposit_count_7d': players['deposit_count_7d'],
'withdrawal_request': players['has_pending_withdrawal'].astype(int),
'bonus_expiry_ignored': players['bonus_expiry_ignored'].astype(int),
'support_complaint': players['has_support_complaint'].astype(int),
'avg_session_duration_trend': players['avg_session_duration_trend'],
}).fillna(0)
def predict(self, players: pd.DataFrame) -> pd.DataFrame:
X = self.build_features(players)
probs = self.model.predict_proba(X)[:, 1]
result = players[['player_id']].copy() if 'player_id' in players.columns else pd.DataFrame(index=players.index)
result['churn_prob_30d'] = probs
result['churn_tier'] = pd.cut(probs, bins=[0, 0.2, 0.5, 0.75, 1.0],
labels=['low', 'medium', 'high', 'critical'])
return result
class BonusCampaignOptimizer:
"""Оптимизация бонусных кампаний"""
def design_retention_campaign(self, player_segment: str,
player_stats: dict,
budget_per_player: float) -> dict:
"""Бонусное предложение под сегмент и бюджет"""
campaigns = {
'champion': {
'bonus_type': 'cashback_vip',
'value': min(player_stats.get('avg_weekly_ggr', 50) * 0.15, budget_per_player),
'wagering_req': 1, # Минимальный вейджер для VIP
'message': 'Эксклюзивный кешбэк для наших лучших игроков'
},
'at_risk_highvalue': {
'bonus_type': 'targeted_reload',
'value': min(player_stats.get('avg_deposit', 100) * 0.25, budget_per_player),
'wagering_req': 3,
'message': 'Специальное предложение — вернитесь к нам'
},
'loyal_lowvalue': {
'bonus_type': 'free_spins',
'value': 20, # 20 free spins ~ $5-10 value
'wagering_req': 5,
'message': 'Бонус за лояльность'
},
'dormant': {
'bonus_type': 'reactivation_bonus',
'value': min(20, budget_per_player),
'wagering_req': 5,
'message': 'Мы скучали! Вот бонус за возвращение'
}
}
campaign = campaigns.get(player_segment, campaigns['loyal_lowvalue'])
# RG проверка: не выдаём бонусы игрокам с высоким RG-риском
if player_stats.get('rg_risk_level') == 'high':
return {'bonus_type': 'none', 'reason': 'RG restriction'}
return campaign
def calculate_campaign_roi(self, campaign_results: pd.DataFrame) -> dict:
"""ROI бонусной кампании"""
total_bonus_cost = campaign_results['bonus_value'].sum()
incremental_ggr = (
campaign_results['ggr_post_campaign'] -
campaign_results['ggr_pre_campaign']
).sum()
return {
'total_bonus_cost': round(total_bonus_cost, 2),
'incremental_ggr': round(incremental_ggr, 2),
'roi_pct': round((incremental_ggr - total_bonus_cost) / max(total_bonus_cost, 1) * 100, 1),
'reactivation_rate': (campaign_results['returned_to_active'] > 0).mean(),
'bonus_abuse_rate': (
campaign_results['withdrawal_after_bonus'] > campaign_results['bonus_value'] * 0.9
).mean()
}
An iGaming CRM with AI segmentation increases the effectiveness of bonus campaigns: ROI increases from 80-120% to 150-250% by targeting the right segments. Key point: the champion segment (5-10% of the base, 50-70% GGR) requires a dedicated manager, not mass emails.