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A Room with a View: Understanding

Users’ Stages in Picking a Hotel Online

Abstract

We describe how we built a model for user decision-

making during local search tasks, specifically hotels. We

differentiate between affective and functional needs and

identify the following stages and related information

needs: 0. Lay of the land; 1. Generating options; 2.

Scanning for attractors and detractors; 3. Due

diligence. We contrast this framework with existing

consumer decision-making models. We close by

describing how this model influenced the development

of the recently launched experiment, Google Hotel

Finder.

Keywords

Local search; consumer decision-making; online search

ACM Classification Keywords

H.5.2. [User Interfaces]: User-centered design;

General Terms

Design, Human Factors

Introduction

A quick comparison of a set of typical local searches,

such as ‘shops’, ‘bars, ‘restaurants’, ‘hotels’ shows that

searches for hotels were the most frequent of this

group in the US in 2010 [2]. Yet choosing a hotel

CHI 2012, May 5–10, 2012, Austin, TX, USA.

ACM 978-1-4503-1016-1/12/05

Jens Riegelsberger

Google UK

76 Buckingham Palace Road

London, SW1W 9TQ, UK

[email protected]

Michelle Lee

Google Inc.

76 Ninth Avenue

New York, NY 10011, US

mishmosh@gmail.com

Scott Lederer

Google Inc.

76 Ninth Avenue

New York, NY 10011, US

scottleder[email protected]

1 2 3 4

Indice de contenidos

Pagina 1 - Introduction

A Room with a View: Understanding Users’ Stages in Picking a Hotel Online Abstract We describe how we built a model for user decision-making during

Pagina 2 - Findings

should be less frequent than choosing a restaurant: for 3-day trip, a traveler might choose 3 restaurants, but stay at only 1 hotel. The disproportio

Pagina 3 - Implementation

Figure 1: 4 stages of hotel search STAGES. Across participants we could see stable stages of decision-making (Figure 1). Note that these stages are t

Pagina 4 - Conclusions

users not familiar with an area, highlighting busy areas vs. residential ones. Location / Price / Quality: An editable polygon allows filtering on lo

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