A C A S E S T U D YB Y

P A R A L L E L D O T S

H O WH A P P Y

A R E Y O U RH O T E L

G U E S T S ?

P R O P O S E DB Y E T H A N P E R E Z

O V E R V I E W

EXECUTIVE SUMMARY

We analyzed over fifty thousand open

ended online customer reviews of different

hotels located in major cities of the world.

The reviews were gathered from major

online hotel reservation platforms and

analyzed with the help of our proprietary

state-of-the-art Machine Learning

algorithms.

Our algorithm identifies key themes around

which most of the reviews revolved. We also

analyzed the sentiments, emotions and

important keywords within the reviews to

get a detailed view of what the guests want

to say.

Online reviews and ratings have become a

key factor in the hospitality business. Every

hotel is vulnerable to angry and dissatisfied

customers because a bad online review can

make or break a hotel’s image permanently.

Hotels invest heavily on customer

satisfaction but these efforts are often

scattered and do not hit the mark. Hotel

owners often wonder if a particular

investment in the form of, let’s say, a

renovation project will yield the estimated

output. Through this study we look to

provide a quantitative and data backed

answer to one essential question- What are

your investments worth?

WWW.PARALLELDOTS.COM

The study revealed that different tiers of

hotels have significantly different types

of customers. For instance, a guest

reviewing a five-star property is more

concerned about the facilities as

compared to the value for money.

The study also revealed that consistency

in terms of service is of utmost

importance.

The major challenge with this kind of study

is that the reviews are unstructured (open-

ended). Analyzing unstructured text is time

and resource intensive. ParallelDots

simplifies this task using Machine Learning

and NLP techniques.  

The study was done considering two major

aspects, the first being what city a

particular hotel is located in and second

being what tier the hotel belongs to.  

Our studies generated the following

insights.

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ParallelDots used its AI based custom classifier to identify themes

around which most of the reviews revolve. It automatically classifies

each review into one of these key themes.

The major pain points of guests are:

Location

Service

Facilities

Value for money

Food

Early check-in

Cleanliness

Hospitality

WHAT ARE THE KEY THEMES THAT GUESTSTALK ABOUT?

K E Y I N S I G H T S G E N E R A T E D

HIGHLIGHTS

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An overwhelming number of reviews (over 50% of the total reviews

analyzed) talk about the value a hotel serves compared to the money

they charge. Value for money being the most talked about theme was

mined for keywords as well as underlying emotions. Location, Room

service and Facilities are also talked about a lot in the reviews.

We identified major cities across the world that are known for their high

tourist influx. These are- New York, Paris, London, Tokyo, Hawaii, Dubai and

Amsterdam. We compared them based on their perceived location, room

service and facilities.

H O W D O T H E H O T E L S F A R E A R O U N DT H E S E K E Y T H E M E S ?

Exhibit 1: Fraction of reviews pertaining to each key theme

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Guests are mostly happy about the location of the hotels. All cities have

received almost the same level of positivity when it comes to location.

Around 40% of the guests in Paris and Tokyo are dissatisfied with room

service.

Hotels in Amsterdam appear to be lacking in terms of facilities. Almost 70

percent of their guests are unhappy with their facilities. New York and

Tokyo also performed pretty poorly in this aspect. However, hotels in

Hawaii provide the best experience in terms of facilities.

Our study identified Hawaii as the destination with the best hotels based

on location, room service and facilities.

WHAT ARE IMPORTANT KEYWORDS IN THELOCATION-RELATED REVIEWS?

Location Room Service Facilities

Exhibit 2: Sentiment Analysis of reviews related to location, room service and facilities

Exhibit 2: Sentiment Analysis of reviews related to location, room service and facilities

HIGHLIGHTS

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Most keywords here are green, meaning that guests are mostly happy

with the location of the hotels.

“Beach” was identified as the keyword with the highest relative frequency.

This led us to the conclusion that properties which face the sea, or are

close to a beach will make guests happy. This fact is also corroborated by

the previous insight which identified Hawaii as the city with the best

hotels.

The negative keywords in this cloud can give hoteliers an idea of what

locational aspects to avoid. For instance, noisy locations leads to

dissatisfied guests.

WHAT IS THE GENERAL SENTIMENT OF GUESTSWHO TALK ABOUT VALUE FOR MONEY?

Exhibit 4: General Sentiment of reviews talking about Value for Money

HIGHLIGHTS

ParallelDots mined the text reviews for the underlying sentiment. We

found that most negative reviews made by dissatisfied guests talk about

value for money. 

As is evident from exhibit 4 more than half the reviews revolving around

“Value for money” display a negative sentiment. This is not that surprising

because the monetary charges mostly become a pain when a customer is

dissatisfied due to some or the other reason. 

HIGHLIGHTS

 A considerable fraction of  all reviewers who talk negatively in their

reviews show dissatisfaction towards the amount they are being

charged.

The emotion analysis of such reviews revealed that almost half the

unsatisfied guests have expressed explicit anger in their reviews.

Such analysis by parallel dots can help in creating good re-targeting

strategies for dissatisfied customers. A hotel can re-target the angry and

sad reviewers with discount coupons and a personalized email apology.

Furthermore such an analysis helps a hotel to identify the cause of

negativity and address it quickly and efficiently.

Exhibit 5: Emotion Analysis of negative reviews about Value for Money

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WHICH TIER OF HOTELS SERVE THE HIGHESTVALUE FOR MONEY?

Hotels are primarily differentiated by the tier they belong to. Hotels

belonging to different classes are meant to cater different audiences. The

concept of value for money maybe completely different for a guest in a 5-

star hotel when compared to guest in a 1-star or 2-star hotel. We analyzed

the different tiers of hotels to find out which class of hotels provide the

highest value for money.

Exhibit 6: Value for Money as served by different tiers of hotels

HIGHLIGHTS

Our study revealed that 1-star and 2-star hotels deliver the least value

for money. The reason maybe, lack crucial facilities such as internet

and room service.

We also discovered that 3-star and 4-star hotels serve the highest

value for money.

Guests who frequently stay in 5-star hotels are less concerned about

value for money and more with other aspects of the stay.

CONCLUSION

This is a sample study undertaken by

ParallelDots using some of their

proprietary tools. The aim of the

study was to communicate the

importance of analyzing open-ended

or unstructured reviews/feedback.

ParallelDots generate some cutting

edge results and insights that would

not have been discovered otherwise.

We uncovered the key themes

around which most of the reviews

revolve and also categorized the

hotel reviews into their respective

themes.

Along with carrying out the

categorization, ParallelDots

employed its AI-based tools to

understand the sentiment and

emotion behind the reviews. All of

these metrics helped us transform

the growth strategy that hotels

across the globe can employ.

ParallelDots is the first mixed data AI

platform that allows you to take your

own data, in spreadsheet format, and

to easily understand what drives key

metrics like satisfaction, loyalty, and

revenue.

Contact us today for information on

how you can leverage our approach.

ParallelDots’ intuitive user interface

SmartReader allows you to begin

immediately!

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