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Polls are so unreliable, what can we believe in marketing?









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发表于 2022-10-1 15:09:52 | 显示全部楼层 |阅读模式
Although 77-year-old comrade Biden won the election, the polls made people feel even more unreliable. The promised victory, the promised crush, turned out to be the most tangled election in the history of the United States; the most votes in history, and the second most votes in history. Comrade Jianguo is still cursing. The other party "does not talk about martial arts". It is said that the major polling agencies in the United States have worked hard for four years after being slapped in the face in 2016.

This year’s polls have unprecedented richness of means and unprecedented sample size, as List of Consumer Mobile Number well as various parameter revisions and various high-tech methods. As a result, the slap in the face is unprecedented. Some people ridiculed that it is better to use the age of two people to judge the huge amount of data with so much money - 77 million votes for the 77-year-old and 74 million for the 74-year-old, which is closer to the actual result than the poll. Well, this method is also a high-tech that we have been circulating for thousands of years: throwing shoes. 02 Similar disputes have always been in the business field, that is, should we trust market research and market data.

The mainstream argument, of course, is to speak with data, and everything is market-oriented. But it's actually quite different when it comes to saying it. Some people are clearly opposed to market research, such as Jobs, such as Ford; the most famous is of course: "If I ask customers what they need, they will tell me that I need a faster horse." In China, "speaking with data" and "speaking with market research" is a kind of "political correctness"; the only big entrepreneur who has the courage to say that he does not do research and does not believe in research data should be Mr. Zong Qinghou. In practice, most bosses and marketers are: if the data is good for their own judgment, they believe the data, and if the data is not good for their own judgment.


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