The Future of AI & Automation

AI and Automation Banner

This two-webinar event looks at how organisations are putting AI & Automation to work in market research & insights.

  • Session 1, 10am London (5pm Singapore, 5am New York)
  • Session 2, 10am New York (10pm Singapore, 3pm London)

The webinars have been broadcast and the slides and recordings are available from our Play Again Page.

Session 1, 10am London (5pm Singapore, 5am New York) 
Chair: Ray Poynter

 

Session 2, 10am New York (10pm Singapore, 3pm London) 
Chair: Ray Poynter

 

 

Presentation Outlines

  • Ray Poynter, NewMR, UK,
    An Introduction to AI and Automation
    In this session, Ray sets the scene of where AI and Automation are currently being used in and around market research, the key terms and concepts, the main successes and areas of conflict.
  • Benoit Hubert and Lea Turquier, Ipsos, France
    The Beauty from the Bottom of the Data Lake
    A.I and machine learning algorithms have made giant leaps to help uncover insights in the millions of conversations, text, pictures from surveys or social media. 
Most of these techniques are good at finding the things you already know you’re looking for, in a rather prescriptive “top down” way. They are good at catching the fishes you need. In this webinar, we will show how ML based “bottom up” approaches now enable us to uncover the unexpected “unknown unknowns” that often hidden in data layers. 
Our advanced analytic designs are grounded on the latest achievements in data science and computational linguistics (LDA) to structure millions of conversations, visual analytics to expose the meaning of thousands of pictures and videos. Concrete client cases will illustrate what A.I bottom up approaches can add to market research, to effectively help brands understand consumers and markets the way they truly are, deep from the bottom of the data lake.
  • Janneke van den Bent, SKIM, UK
    (Wo)man vs Machine; from competition to collaboration
    It is a truth universally acknowledged that client budgets and timelines are shrinking whilst the need for quality remains the same. When quantitative processes are taking advantage of AI and automation, is this solution even a possibility for qualitative research given its human-centric nature? If so, what are the trade-offs? Based on the results of a head-to-head competition judged by Danone, comparing machine analysis, human analysis and a mix of the two, this session offers practical insights for qualitative researchers wanting to learn if, how and when to automate.
  • Jackie Tarran, Strategir, France/UK
    L’OR line extends with confidence coming from AI driven sales volume forecasts
    AI based modelling increases confidence in sales volume forecasts built on research samples of a couple of hundred consumers. This case study shows how the Market Shaker AI volumetric model unlocked the potential of a new innovation for L’OR coffee by: 1) Integrating the right context for purchase through a realistic shopping situation; 2) Recreating the multitude of individual behaviours with powerful AI, multiplying the power to predict each one’s behaviour up to 10,000 times; and 3) Considering each buyer as unique throughout the entire modelling process. Thanks to AI’s enhanced understanding of consumers’ behaviour, JDE had the confidence of thousands upon thousands of virtual consumers’ behind the launch of their innovation.
  • Adrian Sanger, Spinsight, representing Aitrak, UK
    Helping Marketers create better customer experiences using AI
    Visual information overload is a problem for consumers, brands and retailers. Scientists have shown that this creates ‘load induced blindness’. We can now measure those vital 2-3 seconds of pre-attentive vision using a combination of eye-tracking science, machine learning and human intelligence. Automated, real-time solutions mean Marketers can use AI to continuous improve design of the store, the shelf, the pack and the advertising. In this webinar Aitrak will showcase studies with the latest advances in machine learning, vision analytics, and cognitive neuroscience revealing how they are delivering predictive eye-tracking results which are fast, accurate and reliable
  • JD Deitch, P2Sample, France
    10 Ways Sample Companies Should be Using Automation
    Automating sample is an important step toward delivering on industry demands for speed, cost savings and quality. Where is the sample industry when it comes to automating processes? Where should we be? This session provides a vision for automation that goes beyond greater efficiency and lower costs, ultimately enabling suppliers to deliver better data and agility for clients. From respondent satisfaction all the way to effective fraud mitigation, using this technology to its full potential can help to build true efficiencies that result in better quality from start to finish.
  • Chris Robson, Deck Chair Data, USA
    Unleash the Snek: The Role of Python in the Future of Insights
    If you have been following the rapid development of AI, Machine Learning and Automation then you will have noticed a common thread — that a large proportion of the advances being made have used the language Python and its associated libraries. For those coming from an Insights or Research background this may not be familiar territory, so in this presentation I will give a gentle introduction to the language and tools, explain how it has become the ‘lingua franca’ for data science and machine learning, and assess it’s strengths, limitations and the alternatives out there. New horizons require new tools, and it is time for the insights industry to ‘level up’ and get stuck into Python! This will be a largely non-technical presentation but with plenty of pointers for getting deeper.
  • Gaelle Bertrand, Kantar Media, UK
    When to automate, when to use AI, and when to rely on humans
    As an experienced user of Automation and AI, Gaelle discusses what AI can and can’t do, and makes the case for Intelligence Amplification