Technological innovations have the greatest success in business when they are entirely ‘client- focused’. Developments in the retail sector, which is consumer-led, are addressing client demand for more personalised, faster and more competitive services. Data Analytics is changing the way in which these services are offered. This trend is now being taken on board by multiple innovators such as academia, start-ups and technology companies.
Data Analytics links to Behavioural Science in its exploration of consumer behaviour, and provides a most fruitful source of information and insight. This conference addresses and explains how both data analytics, especially predictive and real time analytics, and Behavioural Science insights, can be used to better understand and compete in Retail and Consumer Markets. The event will feature high quality case studies, networking sessions and discussions.
Topics Covered Include:
Bernard Marr, Founder and CEO, Advanced Performance Institute
• It’s easy to get caught up in the hype of big data - Huge datasets, fast-moving analytics…
• How do you really identify the most strategic big data applications in your business
• Learn from exciting case studies including Facebook, Spotify, Wimbledon and Rolls Royce
• See how machine learning and AI are transforming the future of big data
Radek Maciaszek, CTO, A4G
Real time bidding is a dominant way or purchasing volumes of online advertising inventory. What was yesterday a domain of high-frequency trading hedge funds became a norm in todays advertising world. I will talk about the fascinating world of RTB and will discuss the business cases as well as technological challenges advertising companies face today.
Ben Houghton, Senior Data Scientist, Advanced Data Solutions (ADS), Barclays
Data Science is fundamental in driving data-led decisions in a multitude of business areas. In this talk, we will discuss the breadth of opportunities for data science available in the finance sector and the methodologies we can employ to make smart decisions. We will also discuss how data science can be made accessible to everyone, not just data scientists.
Timo P Kunz, Data Scientist, catawiki
In a time where AI is the only game in town, traditional methods seem to vanish from the analyst’s toolbox. This talk describes how catawiki uses stochastic modelling and simulation approaches to better understand customers’ behaviour on its auction platform.
Konstantinos (Kostas) Perifanos, Lead Machine Learning Engineer, Argos.co.uk
Demand forecasting for new products is not an easy task. The challenge is how to forecast sales for a product in case of lack of data. Given a database of several hundreds of thousand products and their demand history, we developed an Information Retrieval / Machine Learning approach to solve this problem.
Elisabetta Fersini, Assistant Professor - University of Milano-Bicocca Italy
Nowadays the number of people consulting product reviews on the web before making any purchase has increased. If most reviews are positive the probability of buying a given product increases, while if most reviews are negative almost certainly customers will choose another product. This gives strong incentives for opinion spamming, which refers to the writing fake reviews to intentionally mislead readers. In this talk, we will give an overview of the current methodologies for fake review detection, extending a markov random field model with some concepts about "burstiness of reviews".
John Murray, Data Scientist & Academic Researcher, University of Liverpool/Fusion Data Science
The growth in GPS fitted smartphones, smartcards and other location aware technology, adds an extra dimension to consumer analytics. John Murray will discuss the role of spatial data in gaining additional insights and show, with practical examples, how it can be integrated into your customer database.
Guy Kirby, UK Head of Advanced Analytics, North Highland Consulting
How can companies move away from widely-targeted marketing campaigns and use their existing data more intelligently to laser focus their target on those most likely to convert? This talk looks at how North Highland worked with Express Oil to build a predictive model using 1st party data collected across 350 variables and 3rd party data from over 2,000 variables. Guy will outline how North Highland identified target customers most likely to convert to mechanical services, allowing Express Oil get 7:1 ROI in the first 6 months, convert thousands of customers and generate significant new revenue not previously possible.
Peter Hafez, RavenPack
The emergence of big data in finance has shifted the alpha focus away from being faster to being smarter than the competition. Access to alternative data sources is considered a key input to such process. Peter Hafez, Chief Data Scientist, will provide an overview of RavenPack's Big Data Analytics and the future development of the RavenPack product suite. In addition, he will present on his latest work on thematic alpha streams as well as providing an overview of general use cases of RavenPack data across various trading and investment applications.