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Mr & Mrs Smith boutique hotels

Mr & Mrs Smith boutique hotels

Mr & Mrs Smith

Travel免费v4.1.9
App Store
评分

4.7

1,155 条评分

星级

★★★★★

最近更新

2026年4月30日

发布日期

2011年6月29日

更新内容

v4.1.9

In this version we've made improvements and bug fixes in the booking flow and improved data security. Thank you for using our app, we wish you a great holiday.

应用信息

开发者
Mr & Mrs Smith
分类
Travel
价格
免费
版本
4.1.9
App ID
445834586

简介

Download the app to discover and book boutique and luxury hotels worldwide. Created by the award-winning hotel experts at Mr & Mrs Smith, this app is tailor-made for discerning travellers in search of the world’s best boutique and luxury hotels. Get instant, on-the-go access to Smith’s unrivalled hand-picked hotel collection and book your stay with ease via Apple Pay in just a few taps. We personally visit every hotel so you can browse thousands of gorgeous images and get insider knowledge of stylish Smith stays. There’s simply no other app packed with this much hotel eye-candy. Welcome, hotel lovers… Features Browse and search Smith’s curated collection of luxury and boutique hotels - Search 100s of destinations in the UK, Europe, the Americas, and Asia-Pacific - Results displayed in map view or as a list - Refine searches by adding number of adults and children in your party - Plan trips by adding hotels to lists of favourites - View room availability with easy-to-navigate calendars Book your trip with ease from end to end - Pay with Apple Pay - Pay using gift cards, e-vouchers or loyalty money - Book your mandatory transfers and extra beds in rooms - No booking or credit card fees Join the Mr & Mrs Smith travel club - Sign in, join as a new user or browse as a guest - Log in via Facebook, Google and Apple - Discover our exclusive offers of up to 50% off at hundreds of hotels Manage your booking - Manage your past, present and future trips - Contact us about your booking

下载量预测

专业 · 预览

预估总下载量

86K58K165K
保守估计乐观估计

319

低 / 月

473

预估 / 月

912

高 / 月

基于1,155 条评分
假设评分率1.4%
应用年龄181 个月

基于评分数量 ÷ 类别评分率估算,实际下载量误差可达 ±50%,与 Sensor Tower 方法一致。