Artificial Intelligence and Market Research

Register here for Session 1, 10am London (5am New York, 5pm Singapore)

Register here for Session 2, 10am New York (3pm London, 10pm Singapore)

Session 1, 10 am London
Chair: Sue York

Photo of Tom De Ruyck Tom De Ruyck & Steven Debaere
InSites Consulting
Minority Report in Research Communities – The future can be seen, member disengagement can be prevented
Photograph of Steven Debaere

Gaelle Bertrand
Kantar Media
Human vs. Machine: working together in harmony?


Photograph of Isabelle Goisbault Isabelle GoisbaultLouise Bardot
Strategir
See your potential buyers mushroom by applying Artificial Intelligence to sales volume forecasting
Photo of Louise Bardot

Photo of Tim BrandwoodTim Brandwood
Digital Taxonomy
Coding open-ended text: The rise of the coding robots?


Register for Session 1 here.

 

Session 2, 10 am New York
Chair: Ray Poynter

Jenn Hirsch profile pictureJenn Hirsch
EY
Living in an AI Future


Lilian Edwards
University of Strathclyde
Governing AI and Algorithms: Accountability, Transparency, Liability?


Photograph of Parry BediParry Bedi
GlimpzIt
Rise of Creative AI – Machines with the Gift of Human Understanding


Photograph of Andrew JeavonsAndrew Jeavons
Mass Cognition
Words, Documents and Distance: Deep Learning and Semantic Analysis


Register for Session 2 here.

Presentation Outlines

  • Tom De Ruyck and Steven Debaere, InSites Consulting,
    Minority Report in Research Communities – The future can be seen, member disengagement can be prevented
    The future can be seen, crime can be prevented. The 2002 award-winning movie Minority Report described a world in 2054 in which crime can be predicted and prevented. In 2017, thanks to AI, behavioral predictions are already today’s reality. It can be used to tackle fundamental problems. Member disengagement, as identified by a member’s low participation quantity and low-quality contributions, represents a fundamental threat to build healthy long-term research communities. We introduce AI to research communities and demonstrate how AI helps the moderator to predict and prevent member disengagement early on. The future can be seen, member disengagement can be prevented.
  • Gaelle BertrandKantar Media,
    Human vs. Machine: working together in harmony?
    Machine learning and Artificial Intelligence are almost always a consideration in the field of social social media analysis because as a big data source, many believe that the only way to leverage big data is to use AI. In this talk, Gaelle will provide a view of why human analysis is still key when trying  to research consumers through social media conversations and argue why we are in an age of IA (Intelligence Augmentation) rather than pure Artificial Intelligence.
  • Isabelle Goisbault and Louise Bardot, Strategir,
    See your potential buyers mushroom by applying Artificial Intelligence to sales volume forecasting
    Each consumer is unique, but while innovation potential is only measured with traditionally sized samples of consumers, research’s ability to represent the behaviour of all consumers is inevitably restricted. Now the diversity of individual behaviour can be covered much more completely thanks to the application of Artificial Intelligence to a typical robust and representative consumer sample. We will explain how AI magnifies your research sample to form a large scale virtual panel that captures both the range of possible reactions to an innovation and also the myriad of possible behaviours in response to a proposed marketing plan.
  • Tim Brandwood, Digital Taxonomy,
    Coding open-ended text: The rise of the coding robots?
    Machine learning as an idea has been with us for decades. In recent years, active research and raw computing power have led to significant advancements in this field. It now feels like machine learning is coming of age and promises to revolutionize the way many industries work. The impact of all this on Market Research is uncertain. However, it is becoming clear that routine and repetitive tasks are the most open to automation through machine learning.  One such repetitive task is the process of coding open-ended text responses. In his book “The Rise of the Robots”, Martin Ford asks: “Could another person learn to do your job by studying a detailed record of everything you’ve done in the past? If so, then there’s a good chance that an algorithm may someday be able to learn to do much or all of your job” Does this mean that computers can learn to do Market Research coding instead of humans? For the last year, Tim has (with help) tried to find out the answer to this question. Can a machine really code open ends as well as human? Can modern algorithms magically make sense of what respondents say? Will we need coders in 5 years’ time?
  • Jenn Hirsch, EY
    Living in an AI Future
    Jenn takes us through 4 scenarios of a possible AI driven future using science fiction scenarios to explore how AI will change our lives.
  • Lilian Edwards, University of Strathclyde
    Governing AI and Algorithms: Accountability, Transparency, Liability?
    Lilian has been working with AI for 25 years, and as a Professor of Law has brought forward the legal and ethical considerations for AI, crucial for the market research and data collection space.
  • Parry Bedi, GlimpzIt
    Rise of Creative AI – Machines with the Gift of Human Understanding
    Customers today, whether in B2B or B2C settings, share their lives in pictures, videos and text. These forms of “unstructured data” on one hand represent a treasure trove of insights, but on the other hand have been traditionally hard to analyze. In this session, we’ll provide… a)      A primer on machine learning and how it can be used to obtain a human level understanding of unstructured content at scale. b)      Case studies from VMWare, J&J and NBC Universal where they have used machine learning technology to make important business decisions.
  • Andrew Jeavons, Mass Cognition
    Words, Documents and Distance: Deep Learning and Semantic Analysis
    In the last few years algorithms developed by Google have given researchers powerful new tools to investigate textual data. Using so called “deep learning” neural network techniques it is possible to model the relationships between words, sentences and documents using numerical values to represent the content of the text. The paper presents an overview of the word2vec and doc2vec technologies Google have developed. It then shows examples of semantic mapping using open ended text data. An example of “semantic text clustering” is also shown, this is the application of traditional cluster analysis applied to text using numeric representations obtained from the doc2vec algorithm.

Register for Session 1 here.

Register for Session 2 here.