In the mid-1990s, Target was a discount

Question

In the mid-1990s, Target was a discount superstore behemoth.
The retailer had set itself apart from chief rival Walmart with a focus on more upscale but wallet-friendly fashion and lifestyle lines, spurring double-digit growth by double-digits each year for more than a decade.
That fruitful streak came to an abrupt halt with the United States financial crash in the fall of 2008. Target was hit hard-much harder, in fact, than Walmart. Five years later the company was still struggling.
With more than 1,800 stores and a relatively new e-commerce site, Target was collecting reams of data about its online customers - products purchased, browsing habits, items abandoned in shopping carts- yet it wasn't fully leveraging all that information. The company began to see this huge pile of e-commerce data as the needle-in-a-haystack key to driving higher sales.
Several traditional retailers are indeed tripping over the digital divide. JC Penney has been tanking, with shares recently trading at $1.50. And in October, Sears filed for bankruptcy protection, following the dark road paved by Borders, RadioShack, and Toys 'R' Us. Target, meanwhile, managed a startling recovery from its five-year slump. The company's third-quarter sales increased by more than 5 percent over last year, and in August online revenue showed especially high growth of a whopping 41 percent-the firm's highest gain on record. Fascinated with Target's stunning turnaround, Datar studied the calculated steps it took to fuel its success.

Hire data experts
In 2013, the one bright spot in Target's otherwise bleak financial picture was its then-small e commerce arm. Although overall sales declined, online business soared nearly 30 percent between 2012 and 2013.
Target was awash in customer data from these online sales, but to make sense of it, the company needed to bring in the right people. Paritosh Desai joined Target in August 2013 as vice president of business intelligence, analytics, and testing, and he then went on a hiring spree, growing the analytics team with data scientists and others trained in computer science, math, statistics, and physics, including many who held doctorates.
To attract the best people, Target knew it had to keep at least part of its data operation in Silicon Valley, even though the company's corporate offices were in Minneapolis. "It was a big decision to stay in Silicon Valley," Datar says. "The demand for data-science professionals is through the roof, so you have to go where the experts are. Desai credits the success of data science at Target to this team."
Experiment and execute quickly
Desai created an entrepreneurial culture, knowing experimentation would be critical to discovering how data could be woven into the company's business practices. His colleagues followed this mantra: develop, test, measure. Yet they couldn't just continue with experiment after experiment without applying what they learned-and quickly. "If you just keep experimenting, people   will say, when do the sales come in? You don't have that much time to keep trying," Datar says. "The only solution is to learn fast, take action, and continue to build on your learning."

Deliver a mobile response in milliseconds
Desai knew from previous experience leading data science at Gap Inc. that consumers get frustrated with slow mobile apps. To him, the most important engineering requirement was providing users with a response to their search in milliseconds consistently.
Just as important, the response had to be relevant to that customer. If a consumer searched for "sneakers," the site should not only provide a list of sneaker-like shoes, but at the top of the list should be the particular brand the user purchased in the past.

1 
 A 
 1. The fastest-growing Target's division after the crisis was: 
(A) Traditional off-line retail business
(B) Online retail
(C) None of the above
(D) Own production line

2 
 B 
 2. The rationale to keep analytical team in Silicon Valley: 
(A) Location of corporate offices
(B) Big labor market of data experts
(C) Wide store network in California
(D) None of the above

3 
 A 
 3. The key factor of the data-driven approach success: 
(A) Fast experiments and deploy
(B) Careful experimentation before making decisions
(C) None of the above
(D) Testing stage before deploy

4 
 C 
 4. According to the text, the consumers are frustrated with: 
(A) Lack of search options in the mobile app
(B) Poor quality of goods offered
(C) Low speed of the mobile app 
(D) None of the above

5 
 D 
 5. According to the passage, consumer search top list is: 
(A) The most expensive products in given category
(B) Brands purchased in the past
(C) Products are ordered alphabetically
(D) None of the above

6 
 D 
 6.The primary purpose of the passage is to: 
(A) Describe the situation in Target after the 2008 financial crash
(B) Explain how to reinvent traditional retail
(C) None of the above
(D) Describe how to establish and gain value from e-commerce

arhipceva · Answer

5 and 6 question are not so clear..

Sajjad1994 · Answer

This question is not fit for the purpose of GMAT, moved to the respective forum

In the mid-1990s, Target was a discount

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