Experiment

Introduction Fast product development and rapid innovation require running many controlled online experiments on large user groups. This is challenging on multiple fronts, including cultural, organisational, engineering, and trustworthiness. To address these challenges we need a holistic view of all our systems and their interactions: For a holistic view, don’t just track systems closely related to your experiments. This mitigates the risk of a positive outcome on specific systems translating into a negative global outcome....

Imagine this scenario. You’re on one of several teams working on a sophisticated ride allocation service. Your team is responsible for the core booking allocation engine. You’re tasked with increasing the efficiency of the booking allocation algorithm for allocating drivers to passengers. You know this requires a fairly large overhaul of the implementation which will take several weeks. Meanwhile other team members need to continue ongoing work on related areas of the codebase....

ExP Overview At Grab, we continuously strive to improve the user experience of our app for both our passengers and driver partners. To do that, we’re constantly experimenting, and in fact, many of the improvements we roll out to the Grab app are a direct result of successful experiments. However, running many experiments at the same time can be a messy, complicated and expensive process. That is why we created the Grab experimentation platform (ExP), to provide clean and simple ways to identify opportunities, create prototypes, perform experiments, refine, and launch products....