18:30 monthly update by Philippe Van Impe
• Official opening of the HUB with Alexander De Croo on 20/10
• Nice overview of the trainings (http://www.meetup.com/Open-Data-Innovation-Training-Hub/events/) organized this quarter.
• how to benefit from the DataScience co-working (https://www.linkedin.com/company/10036364) space
• nice list of outstanding job opportunities (http://datasciencebe.com/datajobs/)
19:00 « evolution of the Marketing story seen by marketing executive from fintech »
Gunter Uytterhoeven (https://be.linkedin.com/in/gunteruytterhoeven), Chief Marketing Officer & Head of Transformation at AXA
19:30 «Humanizing data, focusing on the impact of the consumer on how marketing and sales is done today»
Claudine Knop (https://be.linkedin.com/pub/claudine-knop/5/48b/537), Managing Director at DataBase Management
20:00 « architectural view on what is needed to have a nice big data environment generating valuable insights »
Patrick Glenisson (https://be.linkedin.com/in/glenisson), Managing Consultant at Business & Decision
Eric Lecoutre (https://be.linkedin.com/in/lecoutre), Analytical Thinker - Fraud Analytics Domain Leader
20:30 “Test & Improve! Optimise marketing campaigns with an experimental approach”
Speaker is Carl Sablon (https://be.linkedin.com/pub/carl-sablon/1/76b/21b) senior data scientists specialised in marketing.
21:00 Open presentations: « series of open 5 minutes presentation from anyone who has a point of view to share »
• New Startup based at the Hub (http://www.dihub.eu) : pitch from Mimec & Artycs
• New Community: Peter Campbell - Data Innovation for Energy (http://www.meetup.com/Data-Innovation-for-Energy/)
• Open pitch -
21:30 Networking at the Opinio - https://my.vub.ac.be/opinio
Data science – and predictive analytics, in particular – is moving the goalposts for salespeople and marketers alike. In fact, it’s moving the entire stadium. Predictive analytics is the foundational discipline that can make marketing more effective and targeted, and your salespeople better informed and more precise in their messaging.
Predictive analytics leverages a variety of techniques, including statistical analysis, modeling, machine learning, and data mining, to enable us to predict the future.
In recent years, predictive analytics techniques and technologies have helped elect presidents, discover new energy sources, score consumer credit, assess health care risks, detect fraud – even target prospective buyers.
In fact, the Obama presidential win in 2012 was driven at least partially by what is called “uplift,” or persuasion modeling. It’s a form of predictive analytics. Similarly, according to a recent IBM research study, U.S. insurance fraud now costs in excess of $80 billion a year, but companies employing predictive analytics for fraud detection significantly reduce their losses.
Read more at http://www.business2community.com/brandviews/act-on/data-science-transforming-sales-marketing-01007442#2htqTBywixyJpiGZ.99