Advanced Analytics Network

In this sixth ADAN symposium, we present the latest thinking and analytical advances in the use of unstructured text or image data for consumer insight via real-life case studies.

Please arrive between 6pm - 6.30pm for networking and refreshments.

At 6.30pm MRS will reveal its new Data Analytics proposition. We’re expanding our offer for those working in or with an interest in data analytics and we want those attending the symposium to be the first to know.

After this you will hear from 2 presenters with case studies on their use of unstructured data:

18:45 Engineered Insanity? Making sense of footfall patterns at Formula 1 venues

The sheer scale of a Formula 1 weekend presents a huge challenge in terms of understanding fan behaviour, with spectators present for up to 3 or 4 days, but able to roam around an area of several square kilometres. Meshh’s approach to spatial analytics is helping Formula 1 to understand how their fans experience a race weekend, and therefore optimise their approach to a variety of things such as seating areas, merchandise, and spectator activations at a number of circuits.

19:15 Voice Tech & AI – Changing the way brands interact with customer conversations  

There has always been something that gets in the way of our relationship with technology: the keyboard, the mouse, the screen. We’re now ready to analyse the most natural and intuitive form of interaction—the voice. RBS wanted to understand how their senior management felt about their business purpose, goals and strategy and so Kantar’s Analytics Practice used Artificial Intelligence to: structure voice data at scale and decode emotion from voice.

We will finish with a panel discussion with the presenters on key applications/challenges

About ADAN:

ADAN – The Advanced Analytics Network – aims to help identify, develop and disseminate research industry best practice in the general area of advanced analytics.

This covers mainstream multivariate approaches such as conjoint and regression, through to more recently adopted techniques such as random forests and cluster ensemble analysis. We also aim to lead the way within the research industry in identifying optimum approaches for analysing multiple source data.