Excellence in Transformational Use of Data
Marco leads BBVA’s Data & Analytics practice globally, comprising over 350 people across Analytics, Insight and Data Science. His team works across 35 countries, eleven of which are core markets, with a central data hub to deliver algorithms and self-serve tools to support over 20 business units.
Marco has been at the forefront of developing innovative new products for BBVA. One example is the ingesting of new data sources to facilitate a dramatic increase in credit provisioning to SMBs in Spain. This has been achieved with no increase in fraud.
He has also increased the sophistication of BBVA’s modelling capabilities, in order to better understand its propensity models, customer journey, customer lifetime value, and churn, amongst other things. And improved automation means these models now take just days, rather than weeks, to create.
Over the last five years, Sky has transitioned from collecting no data at all, to collecting integrated account, behavioural, online, over-the-top, and third party data. It now holds the largest proprietary data set in Britain, collecting data from 4.5m of Britain’s 26m homes.
Tony and his team are using this data around three key initiatives: AdSmart, a system allowing matrixed, targeted TV advertising, and now responsible for 15% of revenues; viewing data, which Sky now uses to make much more
sophisticated decisions around rights bidding by accurately estimating potential churn; and decisioning around the timing, location, and type of promotions, which has resulted in a 110% uplift in conversion.
Harry has built Barclays’ data science team from the ground up, and put cutting-edge technology in place across the organisation.
Amongst his achievements is the creation of an insights engine that runs 500x faster than any other market product, and developing machine learning capability for mortgage retention, enabling Barclays to identify people who are about to redeem their mortgage – approximately £1bn of assets per
He has also built tools to drive Location Matching, identifying non-Barclays businesses by their card data and triangulating likely matches, as well as putting data governance in place at the bank.
Office for National Statistics