{"id":340433,"date":"2017-05-09T15:16:53","date_gmt":"2017-05-09T07:16:53","guid":{"rendered":"http:\/\/techcrunch.com\/?p=1487004"},"modified":"2017-05-09T15:16:53","modified_gmt":"2017-05-09T07:16:53","slug":"chinas-bike-rental-startups-are-learning-a-lot-about-how-people-spend-their-free-time","status":"publish","type":"post","link":"https:\/\/people.utm.my\/asmawisham\/chinas-bike-rental-startups-are-learning-a-lot-about-how-people-spend-their-free-time\/","title":{"rendered":"China\u2019s bike rental startups are learning a lot about how people spend their free time"},"content":{"rendered":"

<\/p>\n

Editor\u2019s note: This post originally appeared on TechNode<\/a>, an editorial partner of TechCrunch based in China.<\/em><\/p>\n

China\u2019s bike rental startups have hogged the startup limelight and investor dollars this year. The trend has been impossible to ignore, both on the ground and in reporting.\u00a0Ofo became the industry\u2019s first unicorn<\/a>, while Mobike has pulled in over $300 million<\/a>\u00a0in funding this year alone.\u00a0The influx of cash has cast much attention on the space including, principally, how these companies propose to become viable businesses when they charge barely a dollar for a ride.<\/p>\n

Well, we have some\u00a0color on that potential after Mobike released a fascinating report analyzing user data from the recent Labor Day holiday weekend. The insights provide one of the\u00a0first pictures of what\u00a0can be built from bike rental data and an idea of how the company wants to reveal what it knows. Indeed, market intelligence and insight\u00a0could also be an\u00a0area where the\u00a0startup could monetize its business in the future.<\/p>\n

The \u2018May 1st Little Long Holiday Cycling Report\u2019 was generated by the Mobike Big Data Artificial Intelligence Platform \u2014 or Mofang (\u9b54\u65b9 \u2018Magic Cube\u2019) for short \u2014 and it illustrates a change in behavior from Mobike users over the holiday weekend, which ran April 29 to May 1, compared to regular\u00a0weekends and commuting patterns.<\/p>\n

The data falls roughly into two different categories: national trends and behavioral change, or places versus\u00a0people.<\/p>\n

National<\/h2>\n

Overall, during the three-day holiday weekend, usage was up 17.2 percent nationally from the weekend before, with the largest spikes seen in tourist destinations rather than first-tier cities, up 51 percent in Xiamen, 47 percent in Hangzhou. Tianjin saw\u00a0the biggest jump among China\u2019s\u00a0big cities at 36 percent, down to 26 percent for Shanghai which was fifth on the top city list.<\/p>\n

Chengdu had the highest proportion of female riders at 47.7 percent followed by 45.7 percent in Beijing, and 43.3 percent in Shanghai.<\/p>\n

\"\"<\/p>\n

\n

Growth spike for Labor Day weekend compared to previous weekend for Tier 1 cities: Tianjin, Guangzhou, Shenzhen, Beijing, Shanghai (top to bottom)<\/p>\n<\/blockquote>\n

The report tracked the top five ranked cities and there is no mention of the locations which had either very small increases or falls. All in all, the national average was a 17.2 percent increase, but Mofang provided further interpretation of the figures.<\/p>\n

The top five places that attracted the highest\u00a0Mobike use was\u00a0listed as Beijing, Shanghai, Guangzhou, Shenzhen and Chengdu.<\/p>\n

The report includes the \u201ctop 5 hottest tourist destination sites\u201d for visitors who registered their account in other parts of China: Beijing, Shanghai, Guangzhou, Chengdu, and Xiamen. This is where the user tracking starts to become more apparent.<\/p>\n

Behavior<\/h2>\n

The report outlines trends of who is going from which city to where: Beijing and Shanghai users head to Chengdu, while Guangzhou and Shenzhen city folk head to Haikou and Xiamen for a change of pace.<\/p>\n

Then whether users stay in their hometowns or travel to another location. They\u2019re tracked as they head to scenic spots (in their tens of thousands in some cases) and their behavior monitored: they cycle longer than normal, later in the day, visit more places, and in larger groups.<\/p>\n

\"\"<\/p>\n

\n

Graph of use over time of day with the morning peak delayed over the holiday weekend<\/p>\n<\/blockquote>\n

Superficially the data shows the impact the bikes are having on leisure time. The lifestyle advertising seems to have made headway as touristy areas thronged with cyclists. By revealing the data behind the trend, Mofang demonstrates how applying AI to massive amounts of data is going to create a valuable resource for Mobike as it builds a more detailed picture of its users\u2019 lives.<\/p>\n

Selling targeted data to travel companies (or setting up your own) is one thing, but the fact the company is showing that when you head out for a holiday cycle, it knows who you\u2019re cycling with and when, where, at what speed, and whether you\u2019ve traveled together from somewhere else. All this hints at just what else that Magic Cube might glean from its database.<\/p>\n","protected":false},"excerpt":{"rendered":"

\"\" Editor’s note: This post originally appeared on TechNode, an editorial partner of TechCrunch based in China. China’s bike rental startups have hogged the startup limelight and investor dollars this year. The trend has been impossible to ignore, both on the ground and in reporting.\u00a0Ofo became the industry’s first unicorn, while Mobike has pulled in over $300 million\u00a0in… Read More<\/a><\/p>\n

\n<\/img><\/a> <\/img><\/a> <\/img><\/a> <\/img><\/a> <\/img><\/a> <\/img><\/a>\n<\/div>\n

\"\"\/ <\/p>\n","protected":false},"author":5817,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[25],"tags":[59,66,26],"class_list":["post-340433","post","type-post","status-publish","format-standard","hentry","category-technology","tag-media","tag-techcrunch","tag-technology"],"_links":{"self":[{"href":"https:\/\/people.utm.my\/asmawisham\/wp-json\/wp\/v2\/posts\/340433","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/people.utm.my\/asmawisham\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/people.utm.my\/asmawisham\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/people.utm.my\/asmawisham\/wp-json\/wp\/v2\/users\/5817"}],"replies":[{"embeddable":true,"href":"https:\/\/people.utm.my\/asmawisham\/wp-json\/wp\/v2\/comments?post=340433"}],"version-history":[{"count":0,"href":"https:\/\/people.utm.my\/asmawisham\/wp-json\/wp\/v2\/posts\/340433\/revisions"}],"wp:attachment":[{"href":"https:\/\/people.utm.my\/asmawisham\/wp-json\/wp\/v2\/media?parent=340433"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/people.utm.my\/asmawisham\/wp-json\/wp\/v2\/categories?post=340433"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/people.utm.my\/asmawisham\/wp-json\/wp\/v2\/tags?post=340433"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}