Top 10 Customer Friendly Countries based on Google Maps
Creating an Index of “Customer-Friendly Countries” based on data from Google Maps
It all started in Brussels!
Brussels, Belgium - Aug 11, 2018. I was visiting the city for the first time as part of my long waited Euro tour, It was a sunny Saturday morning, I woke up, had my breakfast, and then went to a popular cafe to get my first cup of coffee and guess what … it was a horrible experience!
The staff were rude and inflexible, they had some stupid rules of what can and can’t be done, and add to that long waiting queues to get served. Like any frustrated customer, I went directly to Google Maps to give them a bad review just to find that other people already did.
Stunned with this experience, I had an inner fight; should I judge the whole city with this experience or just ignore and give Brussels another chance :)
I decided to go with the latter, and that’s when an idea💡 sparked in my head …
Can we judge the friendliness of a country by aggregating Review Data from Google Maps ! ?
The Challenge
Answering such question is hard, challenging and tricky. However, this month I was up to it and I managed to conduct research to answer that question using data, following the steps below:
- Define a Hypothesis: “If we took a sample of restaurants and cafes in a country and aggregated the review scores, there will be a significant difference between top and bottom countries” thus finding out friendly vs non-friendly ones
- Fetch the Data: get a statistically significant sample of reviews from Google Maps API for each country — I decided to focus only on restaurants & cafes in capital cities
- Clean the data, for example removing cafes with low number of reviewers
- Analyze the data and make sure the results make sense, this includes statistical tests
- Visualize and summarize the results
More technical and statistical details can be found in the Methodology & Statistics section at the end of the article
Results
The analysis of 153 countries* (capital cities) showed a clear statistically significant gap between the means of aggregated review scores of top and bottom countries.
* 91 countries were excluded due to lack of enough data
I also found the average Google Maps Review score for all countries in the data to be 4.31 with min of 2 and a max of 5
Next, you will find more information about the top and bottom countries
QUIZ: Stop now and think … where do you expect your country to be on the list: top, down or somewhere in the middle ?
Top 10 Countries
Find below the top 10 countries based on average review scores
Bottom 10 Countries
The worst 10 countries based on average review scores
Country Friendliness World Map
Here you can find the world map colored by average review score per country
Below are countries colored by average number of reviews per cafe or restaurant
Country Friendliness Index — Full List
Finally, the full list of all countries and scores …
“Continent” Friendliness Map
Below is the world map with color indicating average review scores aggregated by “Continent”
Statistical significance test
Aside from the clear visual difference in the distribution of the data, a tailed t-test was done on the data points of the top and bottom countries, the results confirmed the initial assumption and the difference in review score means is statistically significant with p-value of: 0.0000000000000002
Below you can see a visualization of the distributions of review scores in both countries
Analysis of User Reviews
Below is a chart showing the top phrases used by reviewers aggregated by review score
We can see some patterns here
- For those who got above average reviews (4.31+), you can see the enthusiasm and much emphasis on “staff friendliness”
- For places scoring average or below average(less than 4.31), there is more focus on “place” and “food” with less enthusiasm (less “very” and “great”)
- Places getting bad reviews between 2–3 is mainly about the price (the data here is not strong since only 3 stores lies in this range)
- Also have you noticed the German text in the red area ? .. anyone who is German or lives in Germany will know why :)
Wall of Fame
Here I show an example of a cafe with an outstanding 5 stars score
I have never thought anyone would get consistent 5 star review from hundreds of people, I am very curious if you — the reader — have an explanation for that !
Two Minutes cafe in Bucharest
Wall of Shame
And now it is time for a bad example from Google Maps
Caffè Vaticano in Rome got consistent ratings of 2 stars, the main pain point seems to be extreme and intransparent prices
Conclusion
At the beginning of this article, I asked the following question
“Can we judge the friendliness of a country by aggregating Review Data from Google Maps ! ?”
Now after the research findings I can confidently say YES but with a small change … as we have seen together (wall of fame/shame) reviews doesn’t always translate to friendliness, it might be about price, cleanliness, speed, etc .. so reviews are about both Friendliness & Service Quality in general, thus I would change the statement to
“Can we judge the friendliness & service quality of a country by aggregating Review Data from Google Maps ! ?”
And this is why the title of the article is “Top 10 Customer Friendly Countries based on Google Maps”
Google Maps is a gold mine
In my opinion, Google Maps is a great source of valuable data which can lead to all kinds of opportunities, for examples you can build applications over Maps to offer smart tagged search based on review text — like “Get me cafes that are not crowded at the moment + usually quiet + plays deep electronic music” **
** Feature request for Google :)
That said, please note that getting data from Google Maps is expensive, I paid around 150 Euros for this research
Do the results make sense?
Define “sense” … personally, the results didn’t confirm my initial bias and I still don’t understand why countries are ranked this way and why 3 countries from the Balkans dominate the top 5 (never been there before)
There might be many factors affecting the results such as the segment of people who dominate the reviews (Tourists probably), the rating culture of the country — like they tend to give good reviews or bad reviews, how developed the country is, the quality of the service industry in that country, and finally the culture and friendliness of the citizens
Let me know in the comments what do you think about these results
How can you personally use this data?
- You can benchmark your store review score against the international average of 4.31 or maybe the local average of your country
- Now you know the list of capital cities that offer: good service, great food and friendly staff :)
- You know what to focus on to reach above average review scores (1- Staff Friendliness 2- Food Quality 3- Place and Price)
Further research
I would be very curious to see how does this data correlate with other indexes like the UN Human Development Index & Zendesk Customer Service Index. If you would like to do further analysis on the data, you can access the aggregated results CSV file here.
Methodology & Statistics
Some detailed technical information about the research
- 21807 data points have been fetched using Google Maps Places APIs
- Only data for restaurants and cafes in each capital city was fetched
- The sample size ranging from 60–120 cafe/restaurant per city was chosen to achieve statistical significance with confidence interval of 95% and 10% margin of error. The total population (number of restaurants per city was assumed to be from 1,000 to 30,000 based on city size)
- Removed any cafe/restaurant with number of reviews less than 10 to avoid bias (16% of data)
- Removed any country which didn’t meet the target sample size of 60 cafe/restaurant — specially after the cleaning step above
- That left only 15727 data points and 153 countries from the initial 242 countries fetched from Google Maps (91 countries excluded because of data size and quality reasons)
Code
I am happy to share the data extraction and analysis code on the following Github repository, however I can’t share the fetched Google Maps data due to terms of service agreement
https://github.com/karimouda/country-friendliness-index
Credits
I would like to thank Hamza Ibrahim, Hany Ouda, Mostafa Nageeb for their time reviewing this article and for the valuable comments.
About Me
I am currently a Freelance Consultant in Berlin in areas of Data & Product. Data is not just my job but also my passion, I started falling in love with data while building my analytics startup in 2012, later I decided to get deeper by doing an MSc in Data Science, I also used to compete on Kaggle.com and every now and then I do fun researches like this one — when I have the time :) You can find more about me here: https://karim.ouda.net
