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Cornell Task: 2016' Xinrui Guo

Xinrui GUO
Professor Jason Hogg
Hackathon
2017.4.28

 
First of all, the author really views the Hackathon event as a great opportunity to not only analyze a Fintech issue within the JD system from a practical perspective but also work with cross culture/expertise teammates and learn. Some key notes taken from the experience are listed as follow.

Cross-culture negotiation and collaboration
 
The very useful first steps would always be building mutual trust and align goals. Instead of jumping to the Hackathon topic, the team started with some introductions, chit-chats, members’ self-introductions (background, current career/academic focus, professional expertise, things to do in the city, etc). Members also went on to align the goals: the Hackathon event is more than a school project and the team would like to devote as much energy as possible to deliver it.

In retrospection, the author finds the beginning discussions very help. It motivates the team towards the same goal and helps members to understand each other’s expertise. Chinese members have purchasing experience on JD.com before and know better about JD’s image, strategies and innovation in various areas. The US members have strong tech background and contribute the most regarding data crunching, solution construction and SQL analysis.

However, the team did run into some challenges when deciding the key issue to solve in the project. The US members suggested JD to learn from Amazon while the Chinese members believed given JD’s self-operated nature and the country’s infrastructure, some sample patterns cannot be installed directly. It was until 6 in the afternoon that the team reached a consensus to utilize machine learn to find out consumers’ unmet needs and high margin areas in which JD should cultivate its own private brand (white label).
 
Suggestions for Future Hackathon
 
In order to identify the real issue and offer solid solution for JD, the team would need real time operational data from the company. When looking at white label, the team could use data regarding the current self-operating to third part goods ratio, the current profit margin, customers’ feedback, developing cost for both groups and financial data on how well the current white label brands are doing.

Throughout previous research and brief exchange with JD professionals, the team found related data still need further specification. The team also tried to get in touch with other JD management team through their own connections. However, given the size of the organization and the complexity of data, it is relatively hard to locate the specific personnel in such a limited time window. The missing of key data made it challenging for the team to locate key areas where JD should go white label.

Thus, for next year’s event, it would be very helpful if data channel is open and real time operational data or maybe mock data could be shared with the teams based on specific topics. It would also be beneficial if through higher administration, each team could have a 20-min informational interview with key personnel regarding specific topic 1 day before the competition.