单选题
What happens when you combine product design virtuosity, high-powered market research techniques, and copious customer data? Too often, the result is gadgets that suffer from "feature creep" or the return of billions of dollars' worth of merchandise by customers who wanted something different at all. That kind of waste is bad enough in normal times. but in a downturn it can take a fearsome toll.
The trouble is that most customer-preference rating tools used in product development today are blunt instruments, primarily because customers have a hard time articulating their desires. Asked to rate a long list of product attributes on a scale of 1 (completely unimportant) to 10 (extremely important), customers are apt to say they want many or even most of them. To crack that problem, companies need a way to help customers sharpen the distinction between "nice to have" and "gotta have".
Some companies are beginning to pierce the fog using a research technique called "Maximum Difference Scaling". "MaxDiff" was pioneered in the early 1990s by Jordan Louviere, who is now a professor at the University of Technology, Sydney. (As with most cutting-edge academic developments, it took time to translate Louviere's research into practical tools.) MaxDiff requires customers to make a sequence of explicit trade-offs. Researchers begin by amassing a list of product or brand attributes—typically from 10 to 40—that represents potential benefits. Then they present respondents with sets of four attributes at a time, asking them to select which attribute of each set they prefer most and least. Subsequent rounds of mixed groupings enable the researchers to identify the standing of each attribute relative to all the others by the number of times customers select it as their most or least important consideration.
A popular restaurant chain recently used MaxDiff to understand why its expansion efforts were misfiring. In a series of focus groups and preference surveys, consumers agreed about what they wanted: more healthful meal options and updated décor. But when the chain's heavily promoted new menu was rolled out, the marketing team was dismayed by the mediocre results. Customers found the complex new choices confusing, and sales were sluggish in the more contemporary new outlets.
The company's marketers decided to cast the range of preferences more broadly. Using MaxDiff, they asked customers to compare eight attributes and came to a striking realisation. The results showed that prompt service of hot meals and a convenient location were far more important to customers than healthful items and modern furnishings, which ended up well down on the list. The best path forward was to improve kitchen service and select restaurant sites based on where customers worked.
The ability to predict how customers will behave can be extremely powerful—and not just when budgets are tight. Companies planning cross-border product rollouts need a tool that is free of cultural bias. And as customer tastes fragment, product development teams need reliable technique for drawing bright lines between customer segments based on the features that matter most to each group. Companies are starting to apply MaxDiff analysis to those issues as well.

单选题 From paragraph one, we can infer that

单选题 According to paragraph two, what should companies do to know customers' preference about product features?

单选题 Which statement is not true about MaxDiff?

单选题 What do the customers need most about the restaurant chain mentioned in the passage?

单选题 What best describes the restaurant chain's expansion efforts?

单选题 What cannot be inferred from the last paragraph?