Talks start 7pm
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Talk1>> Uplift Modelling: Spend Less & Sell More Nick Radcliffe, Stochastic Solutions Limited
DESCRIPTION:
Uplift modelling is a different approach to customer targeting that directly predicts the *change* in customer behaviour — how much more likely a customer is to stay, or to buy, if treated, compared with if they are not. This talk will explain what uplift modelling is, how it works, and show some real-world results, as well as discussing the applicability of the method and offering some tips on how to get the most out of it.
Targeting with uplift models typically generates more sales or more saved customers, while treating fewer of them thus reducing costs. It also helps to identify customers who should definitely not be treated because of negative effects e.g triggering customer attrition.
BIO: Nick is a practising data scientist with over 30 years experience, from neural networks and genetic algorithms on parallel systems in the late 1980s, through parallel machine learning and 3D visualisation software as a founder of Quadstone, from 1995, to novel modelling methods (e.g. uplift modelling) in the early 2000s. Since 2007, he has run Edinburgh data science specialists Stochastic Solutions.
Nick enjoys using his deep knowledge of underlying algorithms to fashion tailored solutions to practical business problems for clients including Barclays, Sainsburys, T-Mobile and Skyscanner, and has a particular interest in testing and correctness in data science.
Nick is also a Visiting Professor of Mathematics at Edinburgh University.
Talk 2 tbc
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Photo by Kari Shea on Unsplash
Talks start 7pm
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7.00: Uplift Modelling: Spend Less & Sell More.
NickRadcliffe, Stochastic Solutions Limited
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7.45 The Anatomy of a Data Science Project
Adam Sroka, Incremental Group
Uplift Modelling: Spend Less & Sell More.
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Uplift modelling is a different approach to customer targeting that directly predicts the *change* in customer behaviour — how much more likely a customer is to stay, or to buy, if treated, compared with if they are not. This talk will explain what uplift modelling is, how it works, and show some real-world results, as well as discussing the applicability of the method and offering some tips on how to get the most out of it.
Targeting with uplift models typically generates more sales or more saved customers, while treating fewer of them thus reducing costs. It also helps to identify customers who should definitely not be treated because of negative effects e.g triggering customer attrition.
BIO: Nick is a practising data scientist with over 30 years experience, from neural networks and genetic algorithms on parallel systems in the late 1980s, through parallel machine learning and 3D visualisation software as a founder of Quadstone, from 1995, to novel modelling methods (e.g. uplift modelling) in the early 2000s. Since 2007, he has run Edinburgh data science specialists Stochastic Solutions.
Nick enjoys using his deep knowledge of underlying algorithms to fashion tailored solutions to practical business problems for clients including Barclays, Sainsburys, T-Mobile and Skyscanner, and has a particular interest in testing and correctness in data science.
Nick is also a Visiting Professor of Mathematics at Edinburgh University.
The Anatomy of a Data Science Project
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Title:
Anatomy of a Data Science Project
This session will walk through what typically goes into a good data science solution and some advice on how to structure a project for ease of understanding and repeatability.
Bio: (https://incrementalgroup.co.uk/about/people/adam-sroka-senior-data-scientist/)
Adam is Incremental’s Senior Data Scientist. He is responsible for developing data and AI solutions for organisations and helping them make sense of large quantities of data so they can drive results.
Adam specialises in predictive modelling and taking large, complex data flows and turning them into robust, manageable pipelines. Adam is passionate about helping organisations realise the importance of good foundational data practices to set them up for AI/ML optimisation in the future. Adam regularly speaks at technology meet up and events on and around this topic.
Adam first realised the power of machine learning during his Doctorate of Engineering at the University of Strathclyde. Since then he has worked as a data scientist in a number of industries including online retail developing predictive models for some of the UK’s most recognisable online and high street retailers. Adam also spent time in insurance delivering complex pricing models and real-time optimisations.
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Photo by Kari Shea on Unsplash
