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.

Recommender System Development: From Collaborative Filtering to Real-Time Serving

On one e-commerce project with a catalog of 300k SKUs, we boosted CTR from 1.8% to 4.4% — a 2.4x increase. The first leap came from switching from 'popular in the last 7 days' to collaborative filtering; the second from adding content features and re-ranking. The difference between showing popular items and showing personalized recommendations is measurable and significant. Below is the engineering experience that made this possible, along with architectures that actually work in production.

Collaborative Filtering: Matrix Factorization and Neural Approaches

Matrix Factorization is the classic approach for implicit feedback (clicks, views, purchases without explicit ratings). ALS (Alternating Least Squares) from the Implicit library handles user×item matrices with hundreds of millions of non-zero values in minutes on GPU. Latent factors 64–256, regularization λ=0.01–0.1 are starting parameters. Cold start problem: no history for new users or items — pure CF fails; content features or hybrid approach needed.

Neural Collaborative Filtering (NCF) replaces the dot product with a neural network. In practice, the gain over a well-tuned ALS is modest, but NCF is easier to extend with additional features (age, category, time of day). Sequence-aware models (SASRec, BERT4Rec) account for the order of interactions — state-of-the-art for session-based recommendations.

How to Choose Recommender System Architecture?

The answer depends on data, load, and cold start requirements. Below are three main approaches with selection criteria.

Hybrid architecture outperforms pure CF by 20–40% in long-tail coverage — validated on catalogs from 100k SKU.

Content-Based Filtering: When Interaction History is Scarce

Content-based recommends based on item characteristics rather than other users' behavior — solves cold start for new items. Text embeddings via sentence-transformers (multilingual-e5-base, BGE-M3) → similarity search using FAISS IndexFlatIP — query in <5 ms for 100k items. Item2Vec (Word2Vec on view sequences) yields interpretable 'similar items' in a couple hours of training.

Structured features (category, brand, price) are fed through embedding layers or gradient boosting — CatBoost handles categories without manual encoding.

Why Hybrid Models Work Better?

Production systems are almost always two-level. Stage 1 (Retrieval) — fast selection of 100–500 candidates from 300k items using ALS or Two-Tower model with vector search (FAISS, Qdrant). Stage 2 (Ranking) — heavy ranker on LightGBM or neural network with cross-features, time, device, and session context. LightFM is a good starting point for medium scale without heavy infrastructure. Our practice shows: moving from single-stage to two-stage yields a 15–25% accuracy improvement with only 20–30 ms additional latency.

Real-Time Serving: Architecture Under Load

Latency SLA — 50–100 ms at thousands of requests per second. Base recommendations precomputed (batch job hourly) → Redis by user_id → <5 ms. Real-time re-ranking via Kafka for events (clicks, cart adds) → update of context features. Feature serving — Redis with TTL (views in 24 hours, last clicked item). At 10k req/s, we deploy Redis Cluster with replication.

A/B testing is the only reliable way to measure improvements. Offline metrics do not always correlate with online. Kohavi et al., 'Online Controlled Experiments at Large Scale' (KDD 2013) — a must-read for the team. Test on 5–10% of traffic, monitor CTR, conversion, revenue per session. One of our client systems after hybridization increased revenue by 18% over a month of A/B.

Recommender System Development Timeline

The stages and typical time frames are in the table below. Costs are calculated individually based on catalog scale and latency requirements.

What's Included in Turnkey Development

1. Data audit — user×item matrix density (typically <0.1%), activity distribution, temporal patterns, cold start statistics.
2. Baseline — 'popular' as a simple threshold that is often hard to beat.
3. Iterative improvement — ALS → content features → two-stage → sequence-aware. Each step with A/B.
4. Serving infrastructure — batch precomputation, Redis, real-time re-ranking, Grafana monitoring.
5. Documentation — model card with metrics, deployment instructions, feature descriptions.
6. Team training — session on interpreting results and model fine-tuning.
7. Support — 1 month post-launch (incident fixes, pipeline tuning).

We are a team with 7+ years of experience in recommender systems, having delivered over 30 projects for e-commerce and media. We guarantee transparent A/B testing and documented metric improvements.

Want to assess the growth potential of your catalog? Contact us for a free data audit. Order recommender system development — first prototype within two weeks.

from implicit.als import AlternatingLeastSquares

model = AlternatingLeastSquares(
    factors=64,
    regularization=0.05,
    iterations=15,
    use_gpu=True
)
model.fit(user_item_matrix)

More about the mathematics of recommender systems — in specialized literature.