Conference Day 2 | 28 March 2018, Wednesday

8:00 AM - 8:55 AM Morning Refreshment and Registration

8:55 AM - 9:00 AM Chairman Opening Remarks

IMPROVING CUSTOMER ENGAGEMENT, PRODUCT & SERVICE OFFERINGS THROUGH MACHINE LEARNING

9:00 AM - 9:40 AM How to: Better understand customers’ behaviour to drive business growth through machine learning

Vivek Nair, Head of Analytics, South East Asia, Visa
  • Using Machine Learning applications and prescriptive analytics to micro-segment customer groups
  • Monitoring customers’ behavior, profiles and patterns to discover actionable insights
  • Providing more personalized and tailored customer service to different customer segment groups
  • How can we leverage on Machine Learning and prescriptive analysis to increase engagement with customers and take pre-emptive measures to raise customer loyalty?


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Vivek Nair

Head of Analytics, South East Asia
Visa

9:40 AM - 10:20 AM Enhancing Carousell’s user experience with deep learning powered features

Lucas Ngoo, Co-founder & Chief Technology Officer, Carousell
  • Improving categorization and classification with deep images features
  • Shortening buyer-seller processing time
  • Personalising the user experience
  • Listing products for sale from 30 seconds to 3 seconds

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Lucas Ngoo

Co-founder & Chief Technology Officer
Carousell

10:20 AM - 11:00 AM Morning Tea and Networking Break

11:00 AM - 11:40 AM Enhancing user experience with machine learning powered personalisation

Nikolay Novozhilov, Head of Digital Products, NTUC Link
  • Data beats algorithms. Data enrichment.
  • What do you do, if you are not Amazon? The problem of cold start.
  • Personalisation. Efficient? Overrated? Creepy?
  • Building a model is not enough – how do you bring it to production?

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Nikolay Novozhilov

Head of Digital Products
NTUC Link

11:40 AM - 12:20 PM Improving customer engagement with personalization of recommendation engines

  • Growing the data science team – doing more with limited resources
  • Building and implementing a personalized product-based recommendation engine
  • Overcoming recommendation engine cold start problems
  • Delivering it as a data product to customer

12:20 PM - 1:00 PM Increasing travellers’ conversion rate with personalised recommender systems from product-based to user-based

  • Proposing most-valuable option through predictive analysis and behavioural analysis
  • Moving from product-based recommender system to user-based recommender systems

1:00 PM - 2:00 PM Networking Lunch Break

ENHANCING OPERATIONAL EFFICIENCY & ACHIEVING COST REDUCTION

2:00 PM - 2:40 PM Optimizing route planning with machine learning

  • Reviewing daily delivery performance of drivers daily delivery performance of drivers
  • Improving realistic and more accurate route plans
  • Identifying hidden inefficiencies to promote continuous distribution performance improvement

2:40 PM - 3:20 PM [Case Study] How to: Improve Demand Prediction across 5 Business Ventures for Efficient Inventory Management and Optimized Pricing Strategies with Machine Learning

· Identifying trends, seasonality, calendars and daily consumer patterns to improve demand forecasting
· Incorporating real-time data into forecasting model
· Purchasing the right amount, at the right time, at the right place

3:20 PM - 4:00 PM Afternoon Tea and Networking Break

4:00 PM - 4:40 PM How to: Leverage on Data to Improve Estimated Time of Arrival with Faster Services

  • Evaluating Recency, Frequency, Monetary of rider to segment customers
  • Leveraging on historical data of similar routes to predict estimated time of arrival with actual time taken
  • Results yielded: Improving ride requests by 15% and reducing cancellation by 5%

4:40 PM - 4:45 PM Chairman Closing Remarks