Do you want to pay less for your mobile plan? use it a lot (and ask some friends to do so)
Ok, so you are reading this post because you want to pay less for your mobile plan. Great! But first of all, let me explain that this is an article about how poor visualizations lead to poor decision making (or no decision at all) But… if you stay with me till the end, I promise you will learn something about paying less for your plan ( and making better decisions with visualizations, of course!)
Recently I discovered this visualization published in 2020 by Visualcapitalist.com , a website focused on using data visualizations and infographics to explain economic issues. Great idea, by the way.
The problem was that I think this is a very poor application of visualization techniques. For me, it seems like an interesting theme, well researched and explained in the article but accompanied by a visualization not very useful. Something like: “eh guys, where is the viz we asked you for my article yesterday? We are publishing in an hour!”
Let’s see more closely to understand why I considered there must be a better use of this data, and how I spend some time trying to make it better.
Biases and limitations in the data:
The article ins featuring the research made by Cable.co.uk so I have used the original research to evaluate the data at hand (Visualcapitalist.com does not provide too many details about this research)
Collection issues:
The data does not consider all the countries due to some technical issues explained by the researchers. Not too bad as it’s a small number and not very representative. (Please note that the link redirects to the 2021 results and not 2020 (the number of countries included this year is 230 and not 155 as mentioned by Visual Capitalist) I really appreciate the authors provide a detailed list in the data available for download.
Processing issues:
I haven’t found any problem in particular as the original authors take some time to document how the average prices have been calculated and reference the criteria followed to convert between local currencies and US dollars.
Insights issues:
Here is where I found it more problematic. There is no record in the data, nor a reference in the visualizations or tables used in the article, where I can find the facts that drive the author to sentence: “Researchers have identified several key elements that help explain the cost variation for mobile data between countries”
For me that’s the key of the article so why is there no data supporting this approach? And without data, the visualization becomes a beautiful collection of international flags ( most of them arranged in a way that makes it difficult to learn something)
What explains the cost variation for mobile data between countries?
So, I decided to try a different approach. As learned in my Udacity Data Visualization Nanodegree I focused on defining a good problem statement using the issue tree technique (resulting in what you can see in the illustration)
Why on earth would you spend some time watching a data visualization if not because there is something in it for you? Knowing what countries have cheaper costs is interesting. Which ones are more expensive? (mmm, it depends. is it the country I’m living in now?) Knowing why some countries are cheaper than others.. that’s it!
Not knowing too much about mobile networks but with the clear way of thinking I have gatherer from muy teachers at Udacity I found three possible reasons some countries have cheaper/more expensive costs than others:
- Countries with better infrastructure can offer better prices to the customers than countries with obsolete networks more expensive to maintain.
- Countries where more people use mobile services every day generate economies of scale so individual prices decrease.
- If more companies compete for a certain number of customers prices would be cheaper as a result of companies trying to get a bigger share of the market.
Why the original visualization could do it better:
I think there are at least three big issues in the original visualization
1. There is not enough data.
With just one metric (average price for 1GB) it is almost impossible to facilitate enough context so we can learn what is driving that price. Seems like the idea of the website was to easily reproduce the article published by the original source, just copying the insights in a visual format. (All the other facts like the situation of Canada or the influence of the number of networks are simple annotations not evidence in any data)
2. The visualization is not adequate.
With only one metric I would use a simple bar chart ordering the countries by price. A ranking would be easier to read and understand and if you want you can keep the flags attached to each bar. This way you create a hierarchy that is clearly communicated. Currently, it’s impossible to see all the countries and their values.
3. The visualization is redundant
By using bubbles and their sizes to communicate the prices the authors are repeating the same information twice, as the position in the Y-axis informs the same. As there is no other data available, the X-axis has no meaning except trying to put in as many countries as you can.
How to make it better
Here it comes: this is my proposal using a “Contrasting Values” data story type. And to do it this way I tried to validate which of my hypotheses (derived from the previous issue tree) was true.
But wait. With only one metric. How could you convert the visualization into a scatter plot? Well, I had to find more data but as Andy Kriebel and Eva Murray say in their book “Makeovermonday” :
“Aside from the initial research, it can be helpful to seek out information from secondary sources, such as those used by the original authors or commentary included with the original analysis. ”
I wanted to tell the story, mobile data prices can tell, not only ranking prices and countries. Finding more data wasn’t difficult.
Not too far I found “Worldwide broadband speed league 2021” another research published by Cable.co.uk that was very easy to join with “internet network speeds derived from over 1.1 billion speed tests taken in the 12 months up to 30 June 2021 and spanning 224 countries”.
I was looking for some data about network infrastructure, its development, level of maturity, etc, but the average speed seems to be a good proxy to measure the situation in each country from the user perspective.
Then I found a big list of network providers so I could have the reference of the number of competitors by using a simple count distinct.
Good infrastructure is important to reduce mobile data prices but not enough.
My initial hypothesis was that countries with a more developed infrastructure would present cheaper prices. Using the values and the indexes available in both datasets (Prices Rank for prices and Speed position for infrastructure) you can easily appreciate that most countries are in the middle but many others have low prices without high speed.
So the idea of bad infrastructure higher prices is true but only for some countries ( in particular it’s true for those with the highest prices)
Adding the number of networks operating in the country only confirmed that low competition is a constant for these countries
But in other cases, the available bandwidth is not correlated with cheaper prices
These countries have modern infrastructures expensive to maintain but customers can afford to pay more. Why should companies offer cheaper prices?
Then we have geographies where networks are not so fast and modern, but the population is big and adoption is almost complete. These countries are in the middle but with big differences between moderated prices in Europe (having big differences between european countries, as usual) and the cheapest prices in Asia.
If adoption is near-ubiquitous prices are cheaper even with no high bandwidth
Competition is important as the market forces prices down (see the number of networks in India for example)
As the original article denoted (without data demonstrating it):
“Countries with little to no fixed-line broadband availability, therefore, rely heavily on mobile data provision. In these cases, mobile data is the primary means the population has of getting online, and adoption is often near-ubiquitous. With a saturated market and many competing providers, often accompanied by a low average wage, data pricing in such countries can be exceptionally cheap when compared globally.”
So my three hypotheses have something to do with explaining the cost variation for mobile data between countries but customer usage seems to be the key driver.
Nevertheless, take care of the way you analyze the data. I’m not an expert in mobile telecommunications so using raw data without a clear context could lead to bad decisions. Always ask the experts as the original authors that advised us in this regard when explaining highest prices: “Countries with poor infrastructure tend to use fewer data. With mobile plans that offer smaller data limits, the overall average cost per GB tends to skew higher.”
Next time you see data like this I hope you would think about it this way. You and of course, the friends you will ask to do so. :)
