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How Amazon Prime Day Compares to Other Shopping Bonanzas

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How Amazon Prime Day Compares to Other Shopping Bonanzas

How Amazon Prime Day Compares to Other Shopping Bonanzas

The Chart of the Week is a weekly Visual Capitalist feature on Fridays.

Earlier this week, products were flying off their figurative shelves at a pace that is pretty unusual for the summer. That’s because July 11, 2017 was the third-ever Amazon Prime Day – and in just a 30-hour period, the online retailer was able to offload close to $1 billion of merchandise.

The biggest seller of the day was the Amazon Echo Dot, an entry-level product that allows customers to access the company’s personal voice assistant, Amazon Alexa. With a 30% discount, it outsold thousands of other heavily discounted items, propelling Amazon to a 60% increase in revenue over the previous Prime Day in 2016.

At triple the sales of a normal day, it’s fair to say Amazon Prime Day is big – but just how big, exactly?

How Does Amazon Prime Day Compare?

Set up to reward Amazon’s roughly 80 million Prime subscribers once a year, the $1 billion number is pretty impressive even on a grander scale.

Here are the e-commerce numbers for Black Friday and Cyber Monday for all U.S. retailers in 2016:

Shopping DayRetailerE-commerce sales% Increase (from prev. year)
Amazon Prime Day (2017)Amazon$1 billion (est.)60%
Black Friday (2016)All U.S. Retailers$3.3 billion22%
Cyber Monday (2016)All U.S. Retailers$3.5 billion12%

The fact that Prime Day revenue comes from only one retailer makes it quite impressive – especially considering the day falls in the heat of summer, rather than the holiday shopping season. The 60% growth figure is compelling as well, showing that Prime Day could eventually be the biggest U.S. shopping day of the year.

Singled Out by Alibaba

While Amazon Prime Day favors comparably in the U.S. market, it doesn’t even close when looking at single-day events abroad. That’s because since 2009, Chinese e-commerce leader Alibaba has used the Singles’ Day “holiday” to promote sales during the slow period leading up to the Lunar New Year season.

In 2016, Alibaba raked in a whopping $17.8 billion of revenue from Singles’ Day alone.

See the steady growth of the event below:

Alibaba Singles' Day Revenues over time

The concept of setting a global record for e-commerce sales on Singles’ Day sounds a bit strange to us Westerners, but the hype around Singles’ Day is no joke.

The holiday is now a full-on festival in China, with star-studded ceremonies kicking off the 24-hour sales period. For last year’s event, even Kobe Bryant, David and Victoria Beckham, and band OneRepublic made appearances to help ring it in.

A Prime Opportunity

During just the first five minutes of Singles’ Day in 2016, shoppers spent as much money ($1 billion) as Amazon’s entire 30-hour event in 2017.

While that may seem daunting to match, Jeff Bezos has built his tech empire from taking advantage of big opportunities such as this. As a result, it’s likely that Bezos and Amazon both see Singles’ Day as something to emulate – a full-blown festival that could ring in over $10 billion in revenue in one day for Amazon.

After all, it’s already been done once by Jack Ma, so why can’t it be done again in North America?

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Data Visualization

Assembling the World Country-by-Country, Based on Economy Size

How does the world map change if it gets assembled based on the size of economies, in ascending order of GDP or GDP per capita?

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If you had to sketch a world map, you’d probably start with a place that is familiar.

Perhaps you would begin by drawing your own continent, or maybe you’d focus on the specific borders of the country you live in. Then, you’d likely move to drawing the outlines of neighboring countries, eventually working your way to far and distant lands.

This would be a logical way for anyone to think about such a task, and it gives some insight as to how humans think about the world.

We start with what’s familiar, and build it out until it’s a complete picture.

Assembling the World by Economy Size

What if we assembled a world map in a completely different order?

Today’s two animations come to us from Engaging-Data, and they approach the world map from an alternate angle: assembling countries on the map in the order of their economic footprints.

GDP (Nominal)

The first map, shown below, uses nominal GDP to assemble countries in ascending order:

Country GDP

This version of the map shows the smallest economies first, with the larger economies at the end.

For this reason, the first economies appearing on the map tend to be developing nations, or nations with smaller geographical or demographic footprints.

For example, even though the Falkland Islands are wealthy on a per capita basis, the British Overseas Territory has fewer than 4,000 people, which gives it a minor footprint on a global stage.

GDP per Capita (Nominal)

Now, let’s take a look at the same map, constructed in order of GDP per capita:

Country GDP per Capita

This animation is more cohesive, given that it is not dependent on population size. Instead the order here is based on economic output (in nominal terms) of the average person in each country or jurisdiction.

In this case, developing nations appear first – and at the end, more developed regions (like Europe and North America) tend to fill out.

Note: All rankings here are in nominal terms, which use market rates to calculate comparable values in U.S. dollars, while omitting the cost of living as a factor. GDP rankings change significantly when using PPP rates.

Other Ways to Assemble the World

While assembling nations based on GDP provides an interesting way to look at the world, this same approach can be tried by applying other statistics as well.

We recommend checking out this page, which allows you to “assemble the world” based on measures like population density, life expectancy, or population.

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Maps

Mapped: The Salary Needed to Buy a Home in 50 U.S. Metro Areas

The annual salary needed to buy a home in the U.S. ranges from $38k to $255k, depending on the metropolitan area you are looking in.

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The Salary Needed to Buy a Home in 50 U.S. Metro Areas

Over the last year, home prices have risen in 49 of the biggest 50 metro areas in the United States.

At the same time, mortgage rates have hit seven-year highs, making things more expensive for any prospective home buyer.

With this context in mind, today’s map comes from HowMuch.net, and it shows the salary needed to buy a home in the 50 largest U.S. metro areas.

The Least and Most Expensive Metro Areas

As a reference point, the median home in the United States costs about $257,600, according to the National Association of Realtors.

 Median Home PriceMontly Payment (PITI)Salary Needed
National$257,600$1,433.91$61,453.51

With a 20% down payment and a 4.90% mortgage rate, and taking into account what’s needed to pay principal, interest, taxes, and insurance (PITI) on the home, it would mean a prospective buyer would need to have $61,453.51 in salary to afford such a purchase.

However, based on your frame of reference, this national estimate may seem extremely low or quite high. That’s because the salary required to buy in different major cities in the U.S. can fall anywhere between $37,659 to $254,835.

The 10 Cheapest Metro Areas

Here are the cheapest metro areas in the U.S., based on data and calculations from HSH.com:

RankMetro AreaMedian Home PriceMonthly Payment (PITI)Salary Needed
#1Pittsburgh$141,625$878.73$37,659.86
#2Cleveland$150,100$943.55$40,437.72
#3Oklahoma City$161,000$964.49$41,335.41
#4Memphis$174,000$966.02$41,400.93
#5Indianapolis$185,200$986.74$42,288.92
#6Louisville$180,100$987.54$42,323.15
#7Cincinnati$169,400$1,013.37$43,429.97
#8St. Louis$174,100$1,031.70$44,215.56
#9Birmingham$202,300$1,040.51$44,593.35
#10Buffalo$154,200$1,066.29$45,698.05

After the dust settles, Pittsburgh ranks as the cheapest metro area in the U.S. to buy a home. According to these calculations, buying a median home in Pittsburgh – which includes the surrounding metro area – requires an annual income of less than $40,000 to buy.

Just missing the list was Detroit, where a salary of $48,002.89 is needed.

The 10 Most Expensive Metro Areas

Now, here are the priciest markets in the country, also based on data from HSH.com:

RankMetro AreaMedian Home PriceMonthly Payment (PITI)Salary Needed
#1San Jose$1,250,000$5,946.17$254,835.73
#2San Francisco$952,200$4,642.82$198,978.01
#3San Diego$626,000$3,071.62$131,640.79
#4Los Angeles$576,100$2,873.64$123,156.01
#5Boston$460,300$2,491.76$106,789.93
#6New York City$403,900$2,465.97$105,684.33
#7Seattle$489,600$2,458.58$105,367.89
#8Washington, D.C.$417,400$2,202.87$94,408.70
#9Denver$438,300$2,139.02$91,672.45
#10Portland$389,000$1,987.37$85,173.08

Topping the list of the most expensive metro areas are San Jose and San Francisco, which are both cities fueled by the economic boom in Silicon Valley. Meanwhile, two other major metro areas in California, Los Angeles and San Diego, are not far behind.

New York City only ranks in sixth here, though it is worth noting that the NYC metro area extends well beyond the five boroughs. It includes Newark, Jersey City, and many nearby counties as well.

As a final point, it’s worth mentioning that all cities here (with the exception of Denver) are in coastal states.

Notes on Calculations

Data on median home prices comes from the National Association of Realtors and is based on 2018 Q4 information, while national mortgage rate data is derived from weekly surveys by Freddie Mac and the Mortgage Bankers Association of America for 30-year fixed rate mortgages.

Calculations include tax and homeowners insurance costs to determine the annual salary it takes to afford the base cost of owning a home (principal, interest, property tax and homeowner’s insurance, or PITI) in the nation’s 50 largest metropolitan areas.

Standard 28% “front-end” debt ratios and a 20% down payments subtracted from the median-home-price data are used to arrive at these figures.

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