Sep 23–26, 2019
Moise Convolbo

Moise Convolbo
Reseach Scientist, Rakuten

Website

Moise Convolbo is a data scientist and research scientist at Rakuten, where he’s harnessing the potential of customer data in reaching “zero customer dissatisfaction.” He built a platform called the Rakuten PathFinder, which empowers product stakeholders such as PDMs, managers, and test engineers to focus on specific struggles along the users’ journeys in order to improve the company’s products and measure their business impact. It’s currently used by Rakuten Gora (the #1 golf course reservation site in Japan), Rakuten toto (the #1 lottery betting site), Rakuten O-net (the #1 match-making web service), and Rakuten Keiba (a horse racing betting service). Moise has had a long experience working with data, the cloud, and geodistributed data centers. He’s always been fascinated by what comes next, in terms of utilizing data from strategic data-informed business decisions. He’s active i academia and has spent time as a reviewer for major big data, cloud optimization, and data science journals from ACM, Elsevier, Springer, and the IEEE.

Sessions

1:30pm2:00pm Tuesday, September 24, 2019
Location: 1A 06
Moise Convolbo (Rakuten)
Customer satisfaction is a key success factor for any business. Moise Convolbo highlights the process to capture relevant customer behavioral data, cluster the user journey by different patterns, and draw conclusions for data-informed business decisions. Read more.
2:30pm3:00pm Tuesday, September 24, 2019
Location: 1A 06
Moderated by:
David Boyle (Audience Strategies)
Panelists:
Richard Evans (Statistics Canada), Leah Xu (Spotify), Victoriya Kalmanovich (Navy), Moise Convolbo (Rakuten)
Often, the difference between a successful data initiative and failed one isn't the data or the technology, but rather its adoption by the wider business. With every business wanting the magic of data but many failing to properly embrace and harness it, we will explore what factors our panelists have seen that led to successes and failures in getting companies to use data products. Read more.

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