Atma Business Blog 1

Atma Business Blog

One of my major goals on Atma is to write about the intersection of sound philosophical concepts with successful emerging business models. It is my core belief that business, grounded by moral values, is the most effective way to improve the culture. Starbucks is prime style of this philosophy. With all the rise of the gen-y (and beyond) eras into the business world, you see income as not the sole motive behind corporate and business actions (this could also be considered a cause of the free business model pattern).

Starbucks has used the business lead in many of the arenas – and they did it earlier than others and in a very old distribution medium. First – let’s commend Starbucks on creating a forward-thinking fast-food concept. In a world of value selections and homogenous offerings, Starbucks brought the cafes of Verona to Hillsboro, TX (and even adopted it for drive-thrus). Somehow they were able to create a brand-new successful fast informal concept that persuaded the masses of the “third” place after work and home.

Since perhaps Subway or some of the other sandwich chains, many have attempted and few have been successful in creating a large-scale lasting business in this market. They also taught us that a “tall” is really a “small”. Second – They did it right (see caveat below). They kept an over-focus on quality from natural material sourcing to the end to get rid of the customer experience.

They successfully balanced an offering that may be replicated throughout the united states with an individual customer experience. They didn’t contend on price, they made espresso shops all over the place cool, and grew the entire industry. Independent coffee shops actually grew 40 percent during 2000-2005 (the Starbucks growth period). They never franchised (which could have helped profit), used only cash from functions to open new stores, and grew exclusively through word of mouth (didn’t spend a dime on advertising until lately).

Third – They did good. They instilled the NW vibe throughout their business. They were the first company to offer health care to all employees (including part-time). Although expensive (particularly starting out, VCs didn’t enjoy it), they never wavered on this point. All of their beans are purchased at prices HIGHER than commodity rates through fair trade (and generally direct from producers); they spend a lot of time and effort to help the small plantations throughout the global world.

  • Protecting innovations and trademarks through intellectual property rules
  • Use reporting tools like Cognos to generate the mandatory MIS reports
  • October, November, December $75 plus $7.50 per full-time employee
  • But: Jack is a pilot in the Royal New Zealand Air Force
  • Trade Remedy Law (6563)

They have a basis that’s fairly large in helping the communities they impact. Overall – I believe Howard Schultz did a good job of caring for their workers, the community and creating a game-changing business. CAVEAT: Sure there have been issues that individuals can indicate during their enlargement years (reports of predatory prices, etc..), but by in large, the huge benefits they created outweigh the negatives significantly.

And I know that in the present, from a business standpoint, they face remarkable challenges (over expansion, increased competition). But at the end of your day – they built the business soundly from a business and cultural standpoint, and I believe they’ll endure. Let’s hope Google truly “does no evil” and another generation of big business embraces the Starbucks way. I believe it’s a model that could work, and wish it’s the guideline rather than the exception.

Its Name Is Email. TechnologyRemember the times before email? Not likely. Most organizations started adopting email in the 1990s as a way to facilitate better communication. Apache Spark with Python: Why use PySpark? TechnologyYes, Ancient Mariner style lines, however the problems here are bigger than the Mariners. In this era of digitization, data generated 24/7. When machine learning progressed, enough data to help machines learn became hard to find in the early years.