We always believe in doing things that have a positive impact. Not only impact to organizations we work with, but also impact to our communities, our society. We believe in contributing our skills for social good. This is the founding principle that embedded within our work every single day.
As part of our contribution to the community, we collaborated with MMUI Big Data Lab last week and ran a day of Social Hackathon. The main objective of the hackathon is to help WeCare.ID with their main challenges in getting donors and setting up priorities on which patients need to be donated first, all based on WeCare.ID data.
The hackathon was held for 1 day at University of Indonesia’s MMUI Salemba campus with 4 participating groups that consist of Stream Data Scientist and MMUI Big Data Lab students. A bit of a background, WeCare.ID is an online crowdfunding platform that collects a pool of fund to help patients who can not afford medical bills or are not covered by BPJS thus enabling them to access optimal healthcare.
The hackathon started in the morning where each group had to vote on solving either one of the challenges presented. These challenges are: 1) RFM segmentation to improve the communication with donors using an appropriate channel and content, and 2) Donation affinity analysis based on patient type. Within 8 hour-period, each group must complete the task by presenting their findings and solutions.
Each presentation was scored and evaluated by Pak Dodot Tri Widodo from Stream, Gigih Septianto from WeCare.ID and Pak Rizky Luxianto from MMUI. Each group was only given 8 minutes to present their solution and 5 minutes for Q&A. All of us (who didn’t participated in the hackathon but came to support) indeed learned so much from each group’s presentation. It was quite an intense, but fun learning session!
The final scoring was announced and Denaya’s group came out as the winner for presenting RFM segmentation, which will be useful to send out tailored marketing communication. The other solution presented by Denaya’s team was recommender system using user-based collaborative filtering. This method provides recommendations to the donors on which next patient needs to be donated based on their preferences from historical data.
We hope to do more hackathon or similar activities in the future, because we believe in having an impact thru collaborative actions. We hope this got your inspired in some way 🙂 Wanna see more pictures? Like and check our Facebook Fan Page!