Solving Real Client Problems: Watson, Crockett #MRIA #MRX

mria 2012Welcome to this series of live blogs from the MRIA Sample the Edge conference in St. John’s Newfoundland. All posts appear within minutes after the speaker has finished. Any errors, omissions, or silly side comments are my own.


  • Social media is more than frequency distributions
  • Worst projects are when client was told to “do something in social media”
  • How do researchers take this information and fit it into the problem solving area
  • Find REAL client problems not just “do something”
  • Want to integrate social media data into their existing programs, makes it accessible for clients,  easier for them than simply deviating from everything they already know, gives them a frame of comparison
  • SM data is just another piece of data they are collecting, roll it up into a solution that works for the client
  • Case Study #1: traffic congestion in toronto, clients wanted to know where GTHA residents stood on awareness, perceptions, opinions, priorities, did social media scan and telephone polling
  • Findings: duh…. congestion is an issue and tax payers are frustrated. But are they willing to pay tolls? Parking tax was more positively received, gas tax was more negatively received.
  • They looked at blog publishers and divided people into for/against revenue tools and informed/uninformed
  • MR data is intended to represent the population – opinion leaders, mass market, brand fans, local customers some of which can be seen from blogs, twitter, facebook, communities and in the right places there
  • Case Study #2: Augment mutual funds
  • Need to understand attitudes towards socially responsible investment held by professional investors and general population
  • Started with focus group method. Used social media to develop the discussion guide for the focus group. [fab idea]
  • Social media identified a new segment – thought leaders, current subscribers, mass market investors. Didn’t expect to find financial advisors
  • Many financial advisors thought Sm is a compliance headache best avoided. Two thirds use NO social media tools.
  • SM can more easily bridge the gap between target populations
  • SM is most effective when complementing other research [shouldn’t ALL research be multi-mode?]
  • Case Study #3: Syndicated tracking in telecom monitoring @provider #provider and provider
  • They mapped interactions between different brands. Could see which provider appeared on which part of the map and how much was overlap.
  • Twitter data is very focused and they coded all the tweets using a code frame. e.g., contract, handset, coverage, apps, data plan, billing, brand, question
  • They saw breakout of topics by brand
  • Is there a net promoter score for social media? [yup]
  • You can see recommendations within twitter data
  • Data sources may be difference but this doesn’t mean the analysis approaches need to change [My standard line is “social media research is market research using a different dataset”]
  • SMR can identify communication gaps
  • Lessons learned: SM is best to complement other research. Does SM access your target market. SM can bridge the gap between target populations. The nature and context of conversations is just as important as sentiment. Use tools from your existing research toolbox.
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