The waiting time displayed by the taxi Hailing software is always less than the actual waiting time; the actual taxi price is always higher than the estimated travel price of the software; different mobile phone brands have different taxi Hailing prices
The feeling of these ordinary people when they use online car Hailing is not a personal illusion, but a real possibility. In 2020, sun Jinyun, associate professor of School of management of Fudan University, led the research team to take more than 800 bus trips in five cities and collected data from Didi, Cao Cao, Shouqi, T3, meituan, Gaode and Yangzhao. These data prove the above conclusions.
Under the related news, many netizens said, “do you want to teach research?” Obviously, many people have had this experience, so it’s no surprise. The software shows waiting for two minutes, planning for three or four minutes, estimating 20 yuan and preparing for 25 yuan. This is a personal response. From a pragmatic point of view, it seems that it’s no big deal that the platform data shows a little error.
However, when experts use “big data” to prove that this is a routine commonly used by the platform, it will still make people unhappy. The platform explained that this is because of the underestimation of time caused by road congestion, but this reason is really insulting to the public’s intelligence, and it is also the big data technology of the platform itself.
First, the waiting time and estimated price displayed on the platform are not accurate for non congestion time. Second, congestion is normal in big cities. It’s understandable that congestion occurs every day, and the first day is not allowed. Big data analysis is still not allowed after a few years. Isn’t that insulting to your engineers?
I’m afraid it’s still the platform playing “smart”. Set the waiting time shorter and the estimated price lower, so as to reduce the psychological threshold of calling. Many people may have a similar experience. During the congestion period, if the waiting time is longer than five minutes or even ten minutes, they may have to consider changing their travel mode. The platform artificially reduces the waiting time, which will certainly help passengers to start the call mode.
If this “little smart” of the platform can be tolerated, another thing confirmed by the research team is hard to accept – the data shows that Apple mobile phone users can only get a discount of 2.07 yuan on average, which is significantly lower than that of non Apple users of 4.12 yuan. In addition, apple mobile phone users are more likely to be picked up by drivers of comfortable vehicles (such as special cars, premium cars, etc.), which is three times that of non Apple mobile phone users.
This is the “Apple tax” summarized by netizens. Of course, in fact, this is not aimed at the brand of mobile phone, but at the price of mobile phone. The platform default that the more expensive the mobile phone, the higher the consumption power, so give them better models and less discounts. To put it bluntly, it is to use big data technology to engage in “price discrimination”. The platform subjectively sets standards and pushes different pricing to different consumers.
From the perspective of platform, it is a means to maximize benefits. But what consumers feel is discrimination and unfairness. Why can’t we call a comfortable car with a non Apple mobile phone and enjoy less discount with an apple mobile phone?
It’s hard for a single consumer to see through and play an effective game. In this regard, thanks to the efforts of the professor’s research team. Because exposing problems is the beginning of solving problems.
Thanks to big data technology, the emergence of taxi Hailing software has improved people’s travel experience, which has long been a consensus. However, when these online car Hailing enterprises gradually gain a firm foothold in the market, I hope they can continue to pay attention to the consumer experience. The profit and development of an excellent enterprise should be based on real services rather than fancy routines.