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Starbucks' Business Growth

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Chris Hulmes

on 12 January 2015

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Transcript of Starbucks' Business Growth

Growth Rate Comparison
Logistical Explanation
Exponential Regression
The above graph represents the growth of Starbucks's amount of businesses through an exponential regression equation
The exponential growth is compared to the actual growth of Starbucks over 13 years
As you can see, both lines are very similar, but towards the end the two begin climb at different paces
This change can be attributed to other unobserved variables
These variables can include changes in trends, economy, market, and availability
Starbucks Data
Starbucks Coffee was first established in 1971 in Seattle, Washington at the Pike Place Market
By the year 1987, there were a total of 17 Starbucks Coffee stores and eventually this number drastically grew to 2498 stores by 1999.
During this period of 13 years, Starbucks simultaneously established itself in the international market at a rapid pace.
The data in the above graph represents the growth of Starbucks' business over this time period.
Using different equations, one can create trend lines which can be used to extrapolate data
Going Deeper
In the year 1998 (x=11), the predicted number of stores is approximately 2,055 compared to an actual value of 1,886.
In the year 1999 (x=12), the predicted number of stores is approximately 3,104 compared to an actual value of 2,498.
Using the exponential regression equation you can predict the number of stores for any x-value (or year).
Why is there a big difference?
Well, because the exponential regression equation leaves out several factors that exist in the real-world
These factors include competition, acquisitions and merges, market availability and saturation, and the well-being of the economy
The above graph represents the logistic equation using the original Starbucks data.
y= 4914.19762
(1 + (269.4594734 x e^(-.4682510109x)))
Top Starbucks Competitors
Logistical Explanation
As you can see, the trend-line is far more accurate in terms of following the actual amount of Starbucks stores. Therefore, the logistic regression equation is the better of the two.
Plus, the logistic equation demonstrates that Starbucks will not grow exponentially forever; it shows that the amount of stores will begin to level out and increase at a much slower pace, meaning that it took certain factors into account.
The exponential regression equation was different as it predicted that the amount of stores would increase at a faster and faster rate infinitely, never slowing down.
Fun Facts
The average Starbucks customer visits the store 6 times per month while a loyal 20% of customers go to the stores 16 times per month
The original Starbucks in Seattle sold coffee in 8 oz cups. During expansion, they added the 12 oz and the 16 oz then got rid of the 8 oz and added the 24 oz and finally the 30 oz
A Starbucks Grande Coffee (16 fl. oz) has 320 milligrams of caffeine, over four times the amount of caffeine in a Red Bull.
Starbucks revenue has tripled in the last ten years reaching $16.5 billion in 2014
At least 60 million Americans visit Starbucks a week
Financial statistics
Worldwide, Starbucks is the top leading coffee franchise generating $14.89 billion in comparison to Tim Hortons ($3.07 billion) and Dunkin Donuts ($0.71 billion)
Total number of Starbucks stores in America is 12,067
Number of Starbucks stores worldwide is over 21k
Just on beverages alone, Starbucks makes $9.36 billion
Starbucks operates in 68 countries and territories
Using this new regression equation, we can predict values once again.
When x=13 (2000), the predicted value is about 3048 stores while the actual amount is 3,501
When x=14 (2001), the predicted value is about 3,553 stores while the actual amount is 4,709
By Chris Hulmes, Mike Waespy, and Mike Sung
Starbucks's Business Growth

y= 4914.19762
(1 + (269.4594734 x e^(-.4682510109x)))
In the case of predicting the amount of stores in the year 2020 (x=33), a different logistic regression equation should be used as the limit of growth in the current one is too low.
Therefore we created a new logistic equation using data up to 2010, making the difference in years 10 instead of 21 and creating a more accurate way of predicting the amount of stores in the future.
Using the new logistic regression equation we find that the number of stores in 2020 is predicted to be 20501 stores.
Rapid Expansion
In this instance, the difference between the predicted and actual values are similar to those found with the exponential regression equation
However, the logistic regression equation still stands as a more accurate method of prediction as it will have prediction values that are closer to actual values as you increase the x-values
For example, if the exponential regression equation had been used, the predicted values would have been 4,688 and 7,082, far more inaccurate than the values given by the logistic equation. The inaccuracy would only increase from there.
I got 99 Problems
(1 + (413.8 * e^(-.339x)))
(1 + (413.8 * e^(-.339x)))
(And Starbucks ain't one)
Starbucks' success can be credited to many factors. These factors can range from the products they sell, their marketing strategies, and even things like interior design.
In a world where people constantly rush around to go to there jobs or other events, a quick cup of coffee is a helpful convenience. The demand for quick coffee can bring success to many business willing to provide it.
Starbucks also provides other coffee-free products that gives it a unique twists among other coffee and donuts shops.
Free wi-fi hotspots and comfort zones provide consumers with an incentive to visit the coffee house and also make up for the relatively steep price of their products.
Furthermore, Starbucks has a diversified revenue stream. While most of its profits come from coffee, Starbucks also sells food items like sandwhiches or pastries, coffee beans, and coffee making machines and equipment to stay financially healthy.
Finally, Starbucks is a major powerhouse internationally with respect to its aggressive approach in expanding into foreign countries
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