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Bandan Singh
Product @ Booking.com - Previously @ Gojek
Bandan Singh is a prominent figure in the fintech sector, currently serving as the Product Lead for Global Expansion of Buy Now Pay Later (BNPL) for Travelers at Booking.com. His expertise lies in developing financial services tailored to enhance customer experiences in the travel industry.
Professional Background
- Current Role: At Booking.com, Bandan focuses on expanding BNPL solutions specifically designed for travelers, leveraging insights from his extensive experience in financial services and product management.
- Previous Experience: Before joining Booking.com, he held significant roles at:
- Riverty: Leading the BNPL business unit, where he developed strategies to enhance financial sustainability and customer-centric services.
- Gojek: As Group Head of Product Financial Services, he contributed to the growth and innovation of financial products.
- Aditya Birla Capital: He gained foundational experience in managing financial services products.
Skills and Insights
Bandan is recognized for his ability to balance macro-level market trends with detailed product development. He emphasizes the importance of understanding local market nuances in BNPL applications, noting that consumer needs vary significantly across regions. His approach integrates customer feedback into product strategy, ensuring that offerings are both relevant and effective.
Thought Leadership
He actively shares his insights through various platforms, including podcasts and newsletters, where he discusses product management strategies and the evolution of fintech products. His work aims not only to drive business success but also to foster positive financial behaviors among consumers.
Bandan's commitment to innovation and customer-centricity positions him as a key player in shaping the future of BNPL services within the travel sector.
Highlights
Experimentation with AI is NOT the same as in traditional product development.
-AI-enabled or AI-first product features and experiences can iterate much faster through different model configurations (multivariate) , vs. traditional product features where you are bound to limited variants and need to wait out the experiment cycle before you get statistically significant results, before you learn setup the next experiment.
-Your zone of collaboration changes. Not just designers and developers, but you need closer working relationship with data scientists and ML engineers.
-Your performance metrics change. While customers metrics still stay the same as an end goal, the specific experiment success criteria changes. You're more concerned with accuracy and precision of the models. And it is even more different in GenAI where it is less about accuracy and precision, but relevance and quality.
-Product stakeholders act as translators between technical data science concepts and business needs. They ensure that the data science team understands the business context and goals.
Here is a full deep dive on how to experiment in the world of AI, where we deep dive into changing mindsets, changing metrics and also diving into aspects of GenAI when experimenting.
Link to full article: https://t.co/1O1vPTy9Fu
