AI-Powered Guest Personalization System for Hotels

A guest checks into a luxury hotel for the third time, yet at the front desk they get a standard "Welcome back." Data from previous visits is scattered: bookings in PMS, reviews in CRM, preferences in the loyalty system — there's no automatic connection between them. As a result, each stay starts from scratch, even though history reveals preferences. Meanwhile, Hilton and Marriott already use AI profiling: the room is configured to preferences before arrival, and offers account for history. The result — RevPAR +15–20%, NPS +12–18 points, and repeat visits grow 1.5 times faster than competitors.

We developed a guest experience personalization system that integrates into your PMS and CRM. The system merges data from PMS, CRM, reviews, and external sources, building a unified guest profile with over 30 characteristics — from preferred floor to average restaurant bill. In 3–4 months, you get a working engine: profile unification, pre-arrival preparation, dynamic pricing. According to McKinsey, a personalized approach increases guest loyalty by 20% and raises the average check by 15% through proactive offers. We'll evaluate your project for free — contact us.

Why hotel personalization is not an option but a necessity?

Repeat guests generate 60% of revenue in the luxury segment. If you don't leverage their history, they leave for competitors. AI addresses three key tasks:

• Pre-arrival preparation: the room is already set up based on preferences (temperature, pillow type, welcome amenities).
• Proactive offers: spa, restaurants, excursions — personalized to the profile.
• Dynamic pricing: loyal guests get discounts, and in high season rates are adjusted by occupancy.

The system pays for itself in less than a year. Average incremental revenue from a personalized guest is $45–80 per night, and marketing cost savings reach $15 per guest through targeting.

How we build the guest profile

We combine data from three sources: booking history, CRM records (loyalty status, dietary), feedback (reviews, ratings). Each profile contains ~30 fields — from typical floor to average restaurant spend.

The code below shows how a unified profile is built and how the LLM generates a personalized email.

import pandas as pd import numpy as np from anthropic import Anthropic import json class GuestProfileManager: """Управление профилем гостя из всех источников данных""" def build_unified_profile(self, guest_id: str, booking_history: pd.DataFrame, feedback_data: pd.DataFrame, crm_data: dict) -> dict: """Объединённый профиль из истории, отзывов и CRM""" guest_stays = booking_history[booking_history['guest_id'] == guest_id] if guest_stays.empty: return {'guest_id': guest_id, 'is_new_guest': True} # Предпочтения из истории profile = { 'guest_id': guest_id, 'is_new_guest': False, 'total_stays': len(guest_stays), 'avg_spend_per_night': guest_stays['revenue_per_night'].mean(), # Предпочтения номера 'preferred_room_type': guest_stays['room_type'].mode().iloc[0] if len(guest_stays) > 0 else 'standard', 'preferred_floor': self._infer_floor_preference(guest_stays), 'prefers_high_floor': (guest_stays['floor'] > 5).mean() > 0.6, 'prefers_quiet_room': guest_stays.get('quiet_room_requested', pd.Series([False])).mean() > 0.5, # Предпочтения питания 'preferred_breakfast': guest_stays.get('breakfast_option', pd.Series(['buffet'])).mode().iloc[0], 'dietary_restrictions': crm_data.get('dietary', []), 'avg_restaurant_spend': guest_stays.get('f_and_b_spend', pd.Series([0])).mean(), # Дополнительные услуги 'typically_uses_spa': guest_stays.get('spa_used', pd.Series([False])).mean() > 0.4, 'typically_uses_gym': guest_stays.get('gym_visits', pd.Series([0])).mean() > 0.5, 'late_checkout_history': guest_stays.get('late_checkout', pd.Series([False])).mean() > 0.3, # Поведенческий профиль 'travel_purpose': self._infer_travel_purpose(guest_stays, crm_data), 'loyalty_tier': crm_data.get('loyalty_tier', 'standard'), } # Анализ отзывов для выявления паттернов guest_feedback = feedback_data[feedback_data['guest_id'] == guest_id] if not guest_feedback.empty: profile['sentiment_themes'] = self._extract_sentiment_themes(guest_feedback) return profile def _infer_floor_preference(self, stays: pd.DataFrame) -> str: if 'floor' not in stays.columns: return 'no_preference' avg_floor = stays['floor'].mean() if avg_floor > 8: return 'high' elif avg_floor < 3: return 'low' return 'mid' def _infer_travel_purpose(self, stays: pd.DataFrame, crm: dict) -> str: if crm.get('company_name'): return 'business' # По дням заезда: пт-вс = leisure, пн-чт = business if 'checkin_weekday' in stays.columns: weekend_ratio = stays['checkin_weekday'].isin([4, 5, 6]).mean() return 'leisure' if weekend_ratio > 0.6 else 'business' return 'mixed' def _extract_sentiment_themes(self, feedback: pd.DataFrame) -> list[str]: positive_reviews = feedback[feedback['rating'] >= 4]['text'].tolist() themes = [] # Упрощённое извлечение тем — в production: NLP topic modeling keywords = {'bed': 'comfortable_bed', 'pool': 'pool_lover', 'service': 'service_focused', 'quiet': 'prefers_quiet', 'breakfast': 'breakfast_fan'} for review in positive_reviews[:10]: for kw, theme in keywords.items(): if kw in review.lower() and theme not in themes: themes.append(theme) return themes[:5] class PreArrivalPersonalizer: """Персонализация до заезда гостя""" def __init__(self): self.llm = Anthropic() def prepare_room_settings(self, guest_profile: dict, available_rooms: list[dict]) -> dict: """Подготовка номера под предпочтения гостя""" preferred_type = guest_profile.get('preferred_room_type', 'standard') prefers_high = guest_profile.get('prefers_high_floor', False) prefers_quiet = guest_profile.get('prefers_quiet_room', False) # Выбор лучшего доступного номера scored_rooms = [] for room in available_rooms: score = 0 if room.get('type') == preferred_type: score += 3 if prefers_high and room.get('floor', 0) > 5: score += 2 if prefers_quiet and room.get('wing') == 'quiet': score += 2 # Лояльные гости получают апгрейд if guest_profile.get('loyalty_tier') in ['gold', 'platinum']: if room.get('is_upgrade_eligible'): score += 1 scored_rooms.append({**room, 'score': score}) best_room = max(scored_rooms, key=lambda x: x['score']) if scored_rooms else {} # Настройки номера к приезду room_setup = { 'room_number': best_room.get('number'), 'temperature_c': 21 if guest_profile.get('travel_purpose') == 'business' else 22, 'pillow_type': 'firm' if 'comfortable_bed' not in guest_profile.get('sentiment_themes', []) else 'soft', 'welcome_amenities': self._select_amenities(guest_profile), 'minibar_stocked': guest_profile.get('avg_spend_per_night', 0) > 150, } return room_setup def _select_amenities(self, profile: dict) -> list[str]: amenities = ['welcome_card'] if profile.get('total_stays', 0) > 5: amenities.append('loyalty_gift') if profile.get('travel_purpose') == 'business': amenities.extend(['bottled_water', 'charging_station']) if profile.get('typically_uses_spa'): amenities.append('spa_welcome_kit') return amenities def generate_pre_arrival_email(self, guest_profile: dict, booking: dict) -> str: """Персонализированное письмо до заезда""" response = self.llm.messages.create( model="claude-3-5-sonnet-20241022", max_tokens=300, messages=[{ "role": "user", "content": f"""Write a personalized pre-arrival email for a hotel guest. Guest: {guest_profile.get('total_stays', 0)} previous stays, {guest_profile.get('loyalty_tier')} member Travel purpose: {guest_profile.get('travel_purpose', 'leisure')} Arrives: {booking.get('checkin_date', 'soon')} Special preferences: {guest_profile.get('sentiment_themes', [])} Write in Russian. Include: 1. Warm personalized welcome (mention loyalty status if gold/platinum) 2. One specific upgrade or perk based on their profile 3. 2 relevant offers (spa/restaurant/local experiences) 4. Check-in info (online check-in available) Avoid generic phrases. Be specific and genuine. 150-200 words.""" }] ) return response.content[0].text class DynamicRevenueOptimizer: """Revenue management с AI персонализацией""" def calculate_personalized_rate(self, guest_profile: dict, base_rate: float, hotel_occupancy: float) -> dict: """Персонализированная ставка с учётом ценности гостя""" # Лояльные гости получают скидку loyalty_discount = { 'standard': 0.0, 'silver': 0.05, 'gold': 0.10, 'platinum': 0.15 }.get(guest_profile.get('loyalty_tier', 'standard'), 0.0) # Динамический коэффициент загрузки if hotel_occupancy > 0.85: occupancy_multiplier = 1.2 elif hotel_occupancy > 0.70: occupancy_multiplier = 1.0 else: occupancy_multiplier = 0.9 final_rate = base_rate * occupancy_multiplier * (1 - loyalty_discount) return { 'base_rate': base_rate, 'personalized_rate': round(final_rate, 2), 'loyalty_savings': round(base_rate * loyalty_discount, 2), 'rate_type': 'member_rate' if loyalty_discount > 0 else 'standard' } 

What components are included in the system?

We deliver a turnkey project:

• Data audit: analyze existing sources, clean, build a DWH.
• Unified profile: pipeline in Python + Spark for daily processing.
• LLM generation: custom prompts for emails and recommendations with RAG-like retrieval.
• Dashboard: monitor metrics (conversion, average check, NPS).
• Integration: API with PMS, CRM, room management system.
• Training: documentation, workshops for the team, one month of post-release support.

Micro-personalization: if a guest mentioned in a review that they liked the croissants and the room was noisy, the system remembers that and next time offers a room in the quiet wing and croissants for breakfast. This boosts loyalty and average spend.

Process

1. Analytics (2-4 weeks): data audit, manager interviews, specification.
2. Design (2 weeks): architecture, LLM selection, prototype on synthetic data.
3. Development (4-8 weeks): profile pipeline, email module, pricing.
4. Testing (2 weeks): A/B test on 10% of guests, comparison with control group.
5. Deployment (1 week): roll out to 100%, monitoring, fine-tuning.

Timeline

Cost is calculated individually based on your stack and data volume. For an accurate estimate, send us a description of your current infrastructure — we will prepare a commercial proposal.

Why choose us?

• 5+ years of experience in AI for hospitality (projects for chain and boutique hotels in Europe and CIS).
• Work with RevPAR as the primary metric — your profit, not feature count.
• Guarantee transparency: you receive not only code but also documentation, dashboards, and a trained team.

Ready to discuss your project? Contact us — we'll evaluate your data for free and provide an implementation plan.