{"id":30674,"date":"2018-11-12T23:51:09","date_gmt":"2018-11-13T04:51:09","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/go-jek-improving-customer-experience-with-machine-learning\/"},"modified":"2018-11-12T23:51:09","modified_gmt":"2018-11-13T04:51:09","slug":"go-jek-improving-customer-experience-with-machine-learning","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/go-jek-improving-customer-experience-with-machine-learning\/","title":{"rendered":"GO-JEK: Improving Customer Experience with Machine Learning"},"content":{"rendered":"

GO-JEK<\/a> is a technology start-up from Indonesia that offers 18+ services under its umbrella. Established in 2010 as a motorcycle ride-hailing phone services, GO-JEK had evolved to a \u201csuper-app\u201d status with services that includes transportation, logistics, mobile payments, food delivery and other on-demand services[1]<\/a>.<\/p>\n

GO-JEK has experienced a hyper-growth of 6600x in the past 3 years, managing over 3 million orders and 2.5 million customers daily today[2]<\/a>. It has more than 1 million drivers on its GO-RIDE and over 200K merchants on its GO-FOOD platforms[3]<\/a>. The sheer scale that the company is operating gives rise to the importance of utilizing machine learning to enhance business efficiency and improve customer experience.<\/p>\n

Machine Learning at GO-JEK<\/strong><\/p>\n

Machine learning plays a key role in helping make decisions real-time at GO-JEK. The company also looks at machine learning to better understand its customers, especially in the context of its recent international expansion strategy; GO-JEK wants to understand the unique needs of customers in different markets in order to offer better products to its customers.<\/p>\n

Driver Allocation<\/em><\/p>\n

Taking driver allocation as an example, the ultimate goal is to improve customer experience when requesting for a ride using the GO-RIDE service. One indicator to track this is the rate of bookings completed. GO-JEK wants to minimize the time a customer has to wait until his requested ride arrived and maximize driver utilization at the same time. Thus, an algorithm is built based on a predetermined set of variables to spit out the best possible driver allocation for the requested ride. This is also coupled with the dynamic pricing model that informs drivers on which areas to service[4]<\/a>. However, the current approach is \u201cpassive\u201d, as it relies on real-time data inputs to churn out an outcome. A customer requesting for a ride in a bad weather condition could be waiting for more than 30 minutes for the ride to come, because of a lack of drivers in the area and the need to call on drivers from other areas to come and made it through traffic. The management action plan going forward is to keep refining the current model so that it could predict demand more accurately based on input features such as weather, time and region to inform drivers in advance on where the rides requests would be. This way, the customer can have a reduced waiting time because there is already enough supply of drivers in the area.<\/p>\n

Home Screen Personalization<\/em><\/p>\n

GO-JEK\u2019s massive customer base of over 25 million monthly active users[5]<\/a>\u00a0uses the app for different purposes. Some used the app for their daily morning commute to work, some used it to order food on Friday nights, some are merchants who used its courier service, and the list goes on. Therefore, it is important for GO-JEK to personalize its home screen to improve customer experience by showing a list of customized recommendations for each customer, rather than having a long feed for everyone. The company applies machine learning in this scenario by taking recorded data that depicts unique user behavior as inputs to precompute a list of personalized recommendations and display them on the user\u2019s home screen each time he opens the app.<\/p>\n

Challenges and Recommendations<\/strong><\/p>\n

The context of operating in emerging markets like Indonesia provides its own unique sets of challenges in scaling up machine learning at GO-JEK. The lack of local talents in the fields of data science has forced the company to maximize its small team of data scientists\u2019 time to take on more projects, build more models and spend less time in data sourcing. As the company expands internationally, I would recommend that the strategy going forward in terms of human resources in scaling up machine learning is to figure out how to increase productivity in its current data scientists, as opposed to hiring more data scientists. Furthermore, building a solid data foundation and ensuring standardization and consistency in data captured would be key for machine learning to work effectively at GO-JEK.<\/p>\n

Open Question<\/strong><\/p>\n