Abstract: 20+ products. Millions of active customers. Insane amount of data and complex domain. Come join me in this talk to know the journey we at Gojek took to predict which of our products a user is most likely to use next.
A major problem we faced, as a company, was targeting our customers with promos and vouchers that were relevant to them. We developed a generalized model that takes into account the transaction history of users and gives a ranked list of our services that they are most likely to use next. From here on, we are able to determine the vouchers that we can target these customers with.
In this talk, I will be talking about how we used recommendation engines to solve this problem, the challenges we faced during the time, and the impact it had on our conversion rates. I will also be talking about the different iterations we went through and how our problem statement evolved as we were solving the problem.
Bio: Gunjan has been working in the industry for 3+ years and has a background in Mathematics. Currently, she is working with the Fraud Team in the Gopay (Gojek) Data Science team. She can talk about statistical models with you all day long and can’t help but notice patterns everywhere in her life. Along with her day job, she also mentors aspiring young data scientists. She currently a mentor at springboard.com for their course Data Science Career Track.