Track Trip.com Hotel Ratings for Hospitality Intel (2026)

Thirdwatch's Ctrip / Trip.com Hotel Scraper extracts hotel ratings, review counts, rating labels, star classifications, and amenity lists from Trip.com across 20+ global cities. Built for hospitality researchers, competitive intelligence analysts, hotel asset managers, and consulting teams benchmarking property quality. Pay-per-result pricing means you pay only for the hotel records you collect, scaling with your research scope.

Why track Trip.com hotel ratings for hospitality intelligence

Trip.com (Ctrip internationally) collects guest reviews from over 590 million annual room night bookings, weighted heavily toward Asian travellers — a demographic whose preferences and satisfaction signals differ meaningfully from the Western-heavy review base on TripAdvisor or Booking.com. For hospitality intelligence, Trip.com ratings reveal how a property performs with its fastest-growing guest segment.

The intelligence job-to-be-done is precise. A hotel asset manager evaluating a portfolio acquisition in Bangkok wants to know where each target property ranks against its competitive set on the platform Asian travellers actually book on. A hospitality consulting firm benchmarking a client's guest satisfaction against 4-star competitors in Singapore needs rating distributions, not just averages. A hotel brand's quality assurance team monitors whether renovated properties are improving their Trip.com scores post-refurbishment. An institutional investor in hospitality REITs uses review velocity (reviews_count growth over time) as a leading indicator of occupancy trends.

All reduce to structured data: rating, reviews_count, rating_label, and stars per hotel, captured at regular intervals and segmented by city, district, and star class. The scraper returns exactly these fields alongside amenities and coordinates for geographic clustering.

How does this compare to the alternatives?

Three approaches to hotel ratings intelligence:

Enterprise reputation platforms aggregate reviews from multiple OTAs, but they charge annual subscriptions and their Trip.com coverage often lags their Booking.com or TripAdvisor data. For Trip.com-specific intelligence -- the platform that matters most for Asia-Pacific hospitality -- the Trip.com Scraper delivers targeted data at a fraction of the cost.

How to track hotel ratings for hospitality intel in 5 steps

Step 1: How do I set up the Apify client?

Install the Python client and authenticate. Sign up at apify.com for a free-tier account:

pip install apify-client
export APIFY_TOKEN="apify_api_xxxxxxxxxxxxxxxx"

import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])

Step 2: How do I pull hotel ratings for a competitive set?

Define your target city and date range. The dates affect which hotels appear (some properties have seasonal availability), but the rating and reviews_count fields reflect cumulative guest scores regardless of dates:

run_input = {
    "city": "Singapore",
    "cityId": 73,
    "checkIn": "2026-08-01",
    "checkOut": "2026-08-03",
    "guests": 2,
    "rooms": 1,
    "maxResults": 100,
}

run = client.actor("thirdwatch/ctrip-hotels-scraper").call(run_input=run_input)
hotels = client.dataset(run["defaultDatasetId"]).list_items().items

print(f"Retrieved ratings for {len(hotels)} hotels in Singapore")

Step 3: How do I segment ratings by star class?

Group hotels by star class to build rating distribution profiles for each tier:

from collections import defaultdict
import statistics

tier_ratings = defaultdict(list)
for h in hotels:
    stars = h.get("stars", 0)
    rating = h.get("rating")
    if stars and rating:
        tier_ratings[stars].append(rating)

for stars in sorted(tier_ratings.keys()):
    ratings = tier_ratings[stars]
    print(f"{stars}-star hotels (n={len(ratings)}):")
    print(f"  Mean rating:   {statistics.mean(ratings):.2f}")
    print(f"  Median rating: {statistics.median(ratings):.2f}")
    print(f"  Std dev:       {statistics.stdev(ratings):.2f}" if len(ratings) > 1 else "")
    print(f"  Range:         {min(ratings):.1f} - {max(ratings):.1f}")

Step 4: How do I identify rating outliers and underperformers?

Find hotels that rate significantly below their star-class peers — these are potential acquisition targets or competitive vulnerabilities:

# Calculate per-tier baselines
tier_baselines = {}
for stars, ratings in tier_ratings.items():
    tier_baselines[stars] = {
        "mean": statistics.mean(ratings),
        "std": statistics.stdev(ratings) if len(ratings) > 1 else 0,
    }

# Flag hotels rating >1 std dev below their tier mean
underperformers = []
for h in hotels:
    stars = h.get("stars", 0)
    rating = h.get("rating", 0)
    if stars in tier_baselines and tier_baselines[stars]["std"] > 0:
        baseline = tier_baselines[stars]
        z_score = (rating - baseline["mean"]) / baseline["std"]
        if z_score < -1:
            underperformers.append({
                "hotel_name": h["hotel_name"],
                "stars": stars,
                "rating": rating,
                "reviews_count": h.get("reviews_count", 0),
                "z_score": round(z_score, 2),
                "district": h.get("district", ""),
                "url": h.get("url", ""),
            })

underperformers.sort(key=lambda x: x["z_score"])
for u in underperformers[:10]:
    print(f"{u['hotel_name']} ({u['stars']}*) — rating {u['rating']} "
          f"(z={u['z_score']}, {u['reviews_count']} reviews) — {u['district']}")

Step 5: How do I track rating changes over time?

Schedule regular snapshots and compare rating and reviews_count changes per hotel_id:

import json
import datetime

def save_snapshot(hotels, filepath):
    snapshot = {
        "timestamp": datetime.datetime.utcnow().isoformat(),
        "hotels": {h["hotel_id"]: {
            "hotel_name": h["hotel_name"],
            "rating": h.get("rating"),
            "reviews_count": h.get("reviews_count", 0),
            "rating_label": h.get("rating_label", ""),
            "stars": h.get("stars"),
        } for h in hotels if h.get("hotel_id")}
    }
    with open(filepath, "w") as f:
        json.dump(snapshot, f)

def compare_ratings(prev_path, curr_path):
    with open(prev_path) as f:
        prev = json.load(f)
    with open(curr_path) as f:
        curr = json.load(f)

    changes = []
    for hid, curr_data in curr["hotels"].items():
        if hid in prev["hotels"]:
            prev_data = prev["hotels"][hid]
            rating_delta = (curr_data["rating"] or 0) - (prev_data["rating"] or 0)
            review_delta = curr_data["reviews_count"] - prev_data["reviews_count"]
            if abs(rating_delta) >= 0.1 or review_delta >= 50:
                changes.append({
                    "hotel_name": curr_data["hotel_name"],
                    "rating_change": round(rating_delta, 2),
                    "new_reviews": review_delta,
                    "current_rating": curr_data["rating"],
                })
    return changes

Sample output

Each hotel record includes the guest satisfaction signals needed for hospitality intelligence:

[
  {
    "hotel_name": "Raffles Hotel Singapore",
    "hotel_id": 231054,
    "url": "https://www.trip.com/hotels/detail/?hotelId=231054",
    "price": 780,
    "currency": "USD",
    "original_price": 920,
    "rating": 4.9,
    "rating_label": "Excellent",
    "reviews_count": 5620,
    "stars": 5,
    "address": "1 Beach Road",
    "city": "Singapore",
    "district": "City Hall",
    "latitude": 1.2942,
    "longitude": 103.8547,
    "image_url": "https://ak-d.tripcdn.com/images/hotel/231054/exterior.jpg",
    "amenities": ["Pool", "Spa", "Fitness Center", "Restaurant", "Bar", "Concierge", "Free WiFi"],
    "room_types": ["Courtyard Suite", "State Room", "Grand Hotel Suite"],
    "tags": ["Heritage Hotel", "Free Cancellation"],
    "distance_from_center": "0.8 km from city center",
    "checkin_date": "2026-08-01",
    "checkout_date": "2026-08-03",
    "search_city": "Singapore",
    "source": "api"
  },
  {
    "hotel_name": "Hotel Boss Singapore",
    "hotel_id": 3492187,
    "url": "https://www.trip.com/hotels/detail/?hotelId=3492187",
    "price": 68,
    "currency": "USD",
    "original_price": null,
    "rating": 3.8,
    "rating_label": "Good",
    "reviews_count": 8940,
    "stars": 3,
    "address": "500 Jalan Sultan",
    "city": "Singapore",
    "district": "Lavender",
    "latitude": 1.3095,
    "longitude": 103.8621,
    "image_url": "https://ak-d.tripcdn.com/images/hotel/3492187/exterior.jpg",
    "amenities": ["Pool", "Restaurant", "Free WiFi", "Laundry"],
    "room_types": ["Superior Room", "Deluxe Room"],
    "tags": ["Best Seller"],
    "distance_from_center": "2.5 km from city center",
    "checkin_date": "2026-08-01",
    "checkout_date": "2026-08-03",
    "search_city": "Singapore",
    "source": "api"
  }
]

The rating (numeric 0-5) and rating_label (qualitative tier) together enable both quantitative analysis and human-readable reporting. reviews_count at 8,940 for Hotel Boss versus 5,620 for Raffles indicates much higher booking volume in the budget segment — useful context for interpreting the rating gap.

Common pitfalls

Rating inflation awareness. Trip.com's 0-5 scale clusters heavily between 3.5 and 5.0 for listed properties. A 4.0 rating is not "good" in absolute terms — it is below the platform median for most cities. Build your benchmarks relative to the city and star-class distribution, not against a theoretical midpoint.

Reviews count is cumulative, not periodic. The reviews_count field reflects all-time reviews, not reviews in a period. To measure review velocity (new reviews per week), you need at least two snapshots separated by a known interval. Subtract previous reviews_count from current reviews_count and divide by the number of days between snapshots.

Star class versus guest rating divergence. A 3-star hotel with a 4.7 rating is outperforming its classification — a signal for investors and brand managers. Conversely, a 5-star hotel with a 4.0 rating has a quality perception gap. The stars and rating fields together reveal this divergence without additional data sources.

Seasonal rating bias. Hotels in beach destinations may score higher during shoulder season (less crowding, better service ratios) than peak season. Compare rating snapshots across the same seasonal windows year-over-year, not across seasons within the same year.

The Thirdwatch actor handles Trip.com's anti-bot protections and client-side rendering so you can focus on the intelligence layer.

Related use cases

• Scrape Trip.com hotel prices for travel research — price-focused extraction for tourism economics research.
• Build a hotel price comparison database with Ctrip — database architecture for ongoing price aggregation.
• Monitor Ctrip hotel availability across cities — availability and rate change monitoring for operations teams.
• Track Booking.com prices for travel research
• The complete guide to scraping travel and hospitality data
• More scraping guides on the Thirdwatch blog.