How Raveable.com works:

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Raveable.com was created because we believe travel planning should be much easier and much more enjoyable!

Throughout our site you will notice that we rank hotels and bring to the surface the common underlying opinions found in millions of individual hotel reviews across the internet. We understand that you may have questions or be skeptical of trusting recommendations made by a website (we would be!). This overview was designed to answer any questions you have about how our website analyzes and rates hotels.

 

Where do we find our information?

We collect information about individual hotels such as the address, list of amenities and pictures from our booking partners and/or the hotels themselves.

Our millions of travel reviews are gathered from all over the internet. Some reviews are collected from well known sites such as TripAdvisor, Expedia, Travelocity, and MyTravelGuide while others come from lesser known sites such as individual travel bloggers.

We already have millions of opinions but we are constantly searching new sites to ensure you have one place to find all of the information you need.

Our ratings and opinion summaries:

We understand that large quantities of information are not useful if the quality of information is low or the information is difficult to understand. The following section describes how we calculate the rankings and opinion summaries found throughout the site.

How we calculate rankings:

Ironically, product and service ratings (or star ratings) were originally intended to help you differentiate one product or service from another. Unfortunately, the vast majority of products and services have an overall rating between 3.5 to 4.5 (out of 5.0) making differentiation difficult.

At Raveable, we address this problem by assigning each property a distinct ranking across the entire city and also within their hotel class. Rankings are located inside of colored (green, orange, red) boxes for each hotel. The rankings show you how the hotel performed amongst other hotels in the same price range but also how the property ranks across the entire city. A simple way to explain our ranking is to imagine each hotel is competing in a race. As they cross the finish line each hotel is ranked in the order they finished with #1 being equivalent to first place.  We chose to display rankings instead of ratings because we found it frustrating when products and services all appear to be above average.

We generate rankings (displayed on the site as numbers inside of colored boxes) for attributes such as service, room and value that are common to almost all hotels. In the spirit of differentiation, our rankings work much like a competition; therefore, the lower the ranking the better the performance.

Our rankings are based off of an average generated for each hotel with enough reviews to be considered statistically valid. The ratings are mathematically defined as a weighted average because we assign differing importance (or weights) to various review factors such as:

• The quality of the site in which the review originated

• The date the review was posted (the newer, the better)

• The quality of the review and the reputation of the author

• The rating of the original review

• Specific comments made inside of each review

Lastly, we normalize the ratings to prevent cases where all hotels in a specific city have nearly the same rating (similar to grading on a curve).

#50 out of 100 is not the same as #50 out of 50:

Unlike other sites, our rankings also take into account whether or not the other hotels you are comparing against have enough reviews to be credible. In other words if there are 100 hotels in the city but only 50 of them have reviews each hotel is ranked against the other 49 hotels not the total population of 100. Of course, Raveable still includes all 100 hotels for you to investigate however a hotel without a ranking indicates that we didn't have enough reviews to accurately rank the hotel.

How we determine the color of the boxes around the ranking:

Each hotel ranking is displayed inside of a green, orange or red colored square. These colored squares were designed to provide an at-a-glance view for each hotel without having to focus on the specific numeric rankings.

In most cases the color of the square that holds the ranking is calculated in the following way.

   Hotels with rankings displayed inside of the green (good) square are ranked in the top 40% of all ranked hotels in the city for that given aspect (ex. Service, Overall etc..).

   Hotels with rankings inside of the orange (average) square are ranked in the middle 20% of all ranked hotels in the city for that given aspect.

   Rankings displayed inside of a red (poor) square mean the hotel is in the bottom 40% of all ranked hotels in the city.

In locations with only a few hotels you may notice a situation where a hotel is ranked 1st but the color of the square is orange. This was designed to prevent a hotel that is ranked #1 out of 3 from earning a green or good square if the overall satisfaction of the hotel is below average. The reverse is also true, if a hotel is ranked last in the city but prior guests have reported above average satisfaction the ranking will be displayed in an orange colored square.

Opinion Summaries:

We believe that most people planning a trip spend most of their time searching and browsing a sea of information trying to determine which hotel is the best choice for their next vacation. With this in mind we developed patent pending technology that automatically analyzes and summaries hotel guest reviews so you can find your perfect hotel quickly and easily.

How we summarize reviews:

We built our technology to summarize guest reviews by giving a few gifted computer engineers too much caffeine and unlimited number of computers to create an artificial intelligence or more specifically natural language process (NLP). This process semantically analyzes portions of each hotel guest review against an ontology to determine the subject matter (i.e. hotel room, location) and the sentiment of the statement (i.e. clean room or dirty room). More simply, our system compares each statement against a database of common words and statements found in hotel reviews. For example phrases like "paper thin walls", "earplugs", "quiet", and "peaceful" are commonly used to describe how noisy or quiet a hotel room is.

Finally, we identify common patterns resulting from this analysis to develop the good and bad summary you see on the site.  

The result is the ability to look at hotels top-down instead of trying to solve a mystery a review or sentence at a time. At a glance we can determine things like the quality of the free breakfast or how good the hotel is for a romantic getaway.

I found a sentence that was incorrectly categorized:

Software is rarely perfect so occasionally you might encounter a situation where a phrase is incorrectly categorized. For example consider the following sentence: "The hotel's idea of excellent customer service and my idea of excellent customer service could not be further apart."It is possible that our software might see the words excellent customer service and make the determination that the sentence is positive.  We are always improving our systems ability to understand the English language, so over time you should see less and less of these errors.  We also put in safeguards to prevent the software from making incorrect recommendations as a result of errors in our language analysis.

How does Raveable account for the inconsistency in the English language (and mistakes made by our engineers)?

In order for a feature to be listed in the "The Good" or "The Bad" section of the web site the feature must have enough evidence from enough credible reviews. We built our system with a margin of error to account for the occasional mislabeling of statements. This not only improves the quality of the recommendations but also means that you are looking at an accurate summary of the underlying data. 

You are the real judge:

None of this matters at all if you do not find Raveable useful. Please, tell us what you like and what you do not so we can improve. You will find that we are hooked on opinions especially those from our own users! Please send an email to support@raveable.com if you have any questions or suggestions on how we can improve.