AI-система предсказания расширения аккаунта (Expansion Prediction)

AI-система предсказания расширения аккаунтов

Account expansion — рост выручки от существующих клиентов через upsell и cross-sell. В B2B SaaS это Net Revenue Retention (NRR): хорошие компании имеют NRR >120%, то есть без единой новой сделки выручка растёт на 20%+ в год. AI определяет, какой аккаунт готов к расширению и почему.

Модель expansion вероятности

import pandas as pd
import numpy as np
from sklearn.ensemble import GradientBoostingClassifier
import shap
from anthropic import Anthropic
import json

class AccountExpansionPredictor:
    """Предсказание готовности аккаунта к расширению"""

    def __init__(self):
        self.model = GradientBoostingClassifier(
            n_estimators=200, learning_rate=0.05, max_depth=4, random_state=42
        )
        self.llm = Anthropic()

    def build_account_features(self, accounts: pd.DataFrame,
                                 usage_data: pd.DataFrame,
                                 support_data: pd.DataFrame) -> pd.DataFrame:
        """Feature engineering для expansion предсказания"""
        features = accounts[['account_id']].copy()

        # === Product Usage Signals ===
        usage = usage_data.groupby('account_id').agg(
            monthly_active_users=('user_id', pd.Series.nunique),
            feature_breadth=('feature_name', pd.Series.nunique),
            sessions_per_user=('session_id', 'count'),
            advanced_features_used=('is_advanced_feature', 'sum'),
        )
        features = features.merge(usage, on='account_id', how='left')

        # Тренд использования за последние 3 месяца
        recent_usage = usage_data[
            usage_data['date'] >= pd.Timestamp.now() - pd.DateOffset(months=3)
        ]
        older_usage = usage_data[
            (usage_data['date'] < pd.Timestamp.now() - pd.DateOffset(months=3)) &
            (usage_data['date'] >= pd.Timestamp.now() - pd.DateOffset(months=6))
        ]

        recent_counts = recent_usage.groupby('account_id')['session_id'].count()
        older_counts = older_usage.groupby('account_id')['session_id'].count()
        usage_trend = (recent_counts - older_counts) / (older_counts + 1)
        features['usage_trend_3m'] = features['account_id'].map(usage_trend).fillna(0)

        # === Account Health ===
        features['days_as_customer'] = accounts.get('days_since_first_purchase', pd.Series([180]))
        features['current_plan_tier'] = accounts.get('plan_tier', pd.Series([1]))  # 1=basic, 2=pro, 3=enterprise
        features['seats_utilization'] = (
            accounts.get('active_users', 1) / accounts.get('licensed_seats', 1)
        ).clip(0, 1)
        features['contract_months_remaining'] = accounts.get('contract_months_remaining', 12)

        # === Support & Satisfaction ===
        support = support_data.groupby('account_id').agg(
            support_tickets_3m=('ticket_id', 'count'),
            avg_csat=('csat_score', 'mean'),
            has_critical_tickets=('priority', lambda x: (x == 'critical').any().astype(int))
        )
        features = features.merge(support, on='account_id', how='left')
        features['support_tickets_3m'] = features['support_tickets_3m'].fillna(0)
        features['avg_csat'] = features['avg_csat'].fillna(3.5)

        # === Expansion Readiness Signals ===
        features['seats_at_capacity'] = (features['seats_utilization'] > 0.90).astype(int)
        features['power_user_count'] = usage_data[
            usage_data['sessions_count'] > usage_data['sessions_count'].quantile(0.90)
        ].groupby('account_id')['user_id'].nunique().reindex(features['account_id']).fillna(0).values

        return features.fillna(0)

    def predict_expansion_opportunities(self, accounts: pd.DataFrame,
                                          usage_data: pd.DataFrame,
                                          support_data: pd.DataFrame) -> pd.DataFrame:
        """Список аккаунтов с высокой вероятностью расширения"""
        features = self.build_account_features(accounts, usage_data, support_data)
        feature_cols = [c for c in features.columns if c != 'account_id']

        X = features[feature_cols]
        probs = self.model.predict_proba(X)[:, 1]

        features['expansion_probability'] = probs
        features['expansion_potential_usd'] = self._estimate_expansion_value(features, accounts)
        features['recommended_product'] = self._recommend_expansion_product(features)

        # Приоритизация для sales team
        features['priority_score'] = features['expansion_probability'] * np.log1p(features['expansion_potential_usd'])

        return features.sort_values('priority_score', ascending=False)

    def _estimate_expansion_value(self, features: pd.DataFrame,
                                    accounts: pd.DataFrame) -> pd.Series:
        """Потенциальный ARR от расширения"""
        base_arr = accounts.get('current_arr', pd.Series([10000]))

        # Seats expansion
        seats_expansion = (
            features.get('seats_at_capacity', 0) *
            features.get('power_user_count', 0) * 50  # $50/seat/month
        )

        # Plan upgrade
        plan_upgrade_potential = (
            (features.get('advanced_features_used', 0) > 5) &
            (features.get('current_plan_tier', 1) < 2)
        ).astype(float) * base_arr * 0.5

        return (seats_expansion * 12 + plan_upgrade_potential).fillna(0)

    def _recommend_expansion_product(self, features: pd.DataFrame) -> pd.Series:
        """Рекомендуемый продукт для расширения"""
        conditions = [
            features.get('seats_at_capacity', pd.Series([0])) > 0,
            features.get('feature_breadth', pd.Series([0])) < 5,
            features.get('current_plan_tier', pd.Series([1])) == 1,
        ]
        choices = ['seat_expansion', 'feature_add_on', 'plan_upgrade']

        result = pd.Series(['general_expansion'] * len(features), index=features.index)
        for cond, choice in zip(conditions, choices):
            result = result.where(~cond, choice)

        return result

    def generate_expansion_brief(self, account: dict) -> str:
        """Бриф для account manager о сигналах расширения"""
        response = self.llm.messages.create(
            model="claude-3-5-sonnet-20241022",
            max_tokens=200,
            messages=[{
                "role": "user",
                "content": f"""Write a sales brief for account expansion in Russian.

Account: {account.get('company_name')}
Current ARR: ${account.get('current_arr', 0):,.0f}
Expansion probability: {account.get('expansion_probability', 0):.0%}
Key signals:
- Seats utilization: {account.get('seats_utilization', 0):.0%}
- Usage trend: {account.get('usage_trend_3m', 0):+.0%}
- Advanced features used: {account.get('advanced_features_used', 0)}
- Power users: {account.get('power_user_count', 0)}
Recommended expansion: {account.get('recommended_product', '')}
Estimated value: ${account.get('expansion_potential_usd', 0):,.0f} ARR

Write 2-3 sentences: what signals you see, what to propose, and how to frame the conversation."""
            }]
        )
        return response.content[0].text

Предиктивный подход к account expansion типично увеличивает NRR на 5-15 процентных пунктов: команда CS фокусируется на top-20% аккаунтов с реальным потенциалом вместо равномерного распределения усилий. Минимальный датасет для обучения: 200+ expansion событий за 12+ месяцев.