Panel: People as Proxy #MRIA16 


Live note taking at #MRIA16 in Montreal. Any errors or bad jokes are my own.

Panel with Sean Copeland, Evan Lyons, Anil Saral, Ariel Charnin, Melanie Kaplan

  • Timelines are very compressed now, instead of two or three months people are asking for hours to get answers
  • It’s no longer 20 minute questions but quick questions
  • Market research is often separate from data science and analytics but this team has put them together
  • They don’t have to answer questions with surveys because they have the raw data and they know the surveys probably won’t be able to answer them accurately; they know when to use market research so that it is most effective
  • When is MR the right solution and when do they partner with data scientists 
  • There is a divide between MR and data science which is strange because our goal of understanding consumers is the same
  • We can see all th transactional data but without MR you miss the why, the motivator, one method doesn’t answer the entire question
  • We need to train and mentor younger researchers [please join http://researchspeakersclub.com ]
  • Some mistrust of quantitative data, are panels rep, why do the numbers change month to month, reexploring Qual to understand the needs and wants, clients remember specific comments from specific focus groups which helps the time to see the issues
  • A doctor is still a doctor even when they use a robot, the same is true for consumer insights with surveys and data science
  • Don’t be protective of your little world, if a project comes to you and is better answered by another method then you are wise to pass it to those people
  • You need to appreciate what MR offers and what analytics offers, both have strengths and weaknesses you need to understand
  • A new language may be morphing out of the combination of MR and data science
  • Everyone believes they are providing insight, of course both sides can do this whether it’s projects and models and understanding the why, insights need to be both of these
  • Still need to be an advocate for MR, can’t just go to data science very time even if it’s the new great toy
  • Live Flow Data – is this a reality, it will happen, can already see 5 day forecast of weather and know about upcoming conferences and how many tickets were sold for a week from now; monthly assumptions from data could happen
  • They can see the effects of ads immediately in live data
  • They don’t want to hear what happened yesterday, need to know what’s happening now
  • Future of our business is understanding people and solving problems, you always need more information to do this; if you learn new things, you can do more things and solve more problems
  • Need more skills in strategy and merging with insights, don’t just hand off reports, help clients take insights and turn them into the next initiative 
  • Is it one story or multiple stories after you’ve got all the data put together
  • Don’t just deliver a product and then leave it, our results are only as accurate as the people who interpret it; research can say a hamburger should look exactly like this but when the end product designers change all the tiny little things to be more convenient then you wine up with a completely wrong hamburger in the end

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