Google A Room with a View: Understanding Users Stages in Picking a Hotel Online - Manual (Page 3)

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 typically done in multiple sessions, spread
over days or weeks.
Stage 0. Lay of the land. Users pass through this
stage only if they have not been to the place before or
know little about it. They read guidebooks, ask friends,
or look online to learn desirable areas and available
parameters (e.g. what is “cheap” in Bermuda).
Stage 1. Generating options. There are numerous
strategies and tools for this stage. A good tool gives the
user the sense that all available options are included
(no one wants to miss out on a good deal) and then
supports quickly trading off location and price, and
discarding low quality candidates.
Stage 2. Attractors and Detractors. Users go
through results, verifying standard attributes (e.g.
price, star classifications, user ratings). They may skim
content rather than read the full text. Crucially, they
also pay attention to unexpected attractors (positive
attributes) and detractors (negative attributes);
recognized explicitly in editorial descriptions or user
reviews, or implicitly in photographs. Importantly,
users could not and did not explicitly state these
attributes at the time of search; they work by
serendipitous recognition rather than by a-priori recall.
Stage 3. Due diligence. This step is labor-intensive,
and only done for a small set of promising options. It
requires resources across the web and beyond. If other
people are involved in decision-making, they are
consulted here: “Is it ok if I book this for us?”
Implementation
The research sketched above led to a number of
fundamental design choices for Google’s recently
launched Hotel Finder. Below, we step through the
stages of our model and map them to some of the
Hotel Finder features. We’d like to emphasize that not
all of these features are unique to Hotel Finder, and
that Hotel Finder is an experimental product, which will
continue to evolve.
Affective needs: Hotel Finder compares current rates
to typical ones, so users can feel good about having
found a good deal. Lay of the land: a heatmap helps