Text Analytics: Summarizing the #TMRE Hashtag traffic

The TMRE 2011 conference wrapped up late Wednesday afternoon.  Several attendees were actively tweeting to the #TMRE hashtag which many of us followed during the event.

Last evening I sat down with @dwiggen’s twapperkeeper archive to pull an RSS feed of the #TMRE for analysis.  The feed as of last evening covered tweets to #TMRE from 11am Monday through 11pm Wednesday (event time).  In total, 2710 tweets were captured; 513 Monday, 1,340 Tuesday and 817 tweets on Wednesday.

As shown on the accompanying graphic,  the popular “tweeting times” were the 9am and 11am hours on Tuesday and Wednesday in sync with the excitement surrounding the keynote sessions.  Peak tweeting volume occurring at 11am on Tuesday.

Sure, there’s a tag cloud …
The tag cloud graphic below was created using Wordle after parsing the feed to remove sender, hashtags, URLs, and stop words.  Retweets were included within the analysis in an attempt to lend additional weighting to the tags.  Just over 36% of total tweets were RTs.

 

“Most Prolific Tweeter” goes to …

Excluding event host TMRE, InsightsGal took the honors and was also retweeted most frequently in total, but notably VirtualMR was retweeted more frequently as a percentage of VirtualMR’s total tweets.

The two most frequently occurring RTs shared the concept of thoughtful interpretation of your survey research analysis:

  • Outsource process, but never outsource thinking. -Stan Sthanunathan of Coke
  • You can’t have breakthrough insights with people who all think the exact same way.

Taking the analysis a step further, we used an algorithm to select a small subset of tweets that are representative of the entire #TMRE tweet stream.  The algorithm scores tweets based upon their weighted tag values after adjusting for tweet length.  This resultant “tweet brief” provides a quick flavoring of the prominent themes within the overall #TMRE tweet stream:

  • rachel_bell44: If you don’t like change you’ll like irrelevance a whole lot less! – Heiko Schafer Henkel/Dial Corp
  • eswayne: #TMRE a lot of people ascribe Coca-Cola’s success to a TV spot, but the real power is the communities of people surrounding the brand.
  • MattIIRUSA: RT @Ali_Saland: Ask yourself, how have u made it simple 4 the brain 2 process? What have u done 2 make the consumer feel better by buying ur product
  • mg_hoban: TMRE=Take More Risks, Everyone! RT “@LoveStats: TMRE=t-tests manovas regressions experiments”
  • CandiceSeiger: RT @klonnie: #TMRE social media Anthony Barton@Intel small amount of research paranoia over SM/time to insights is far shorter with SM

I hope you enjoyed this summary.  If you attended the conference this week, I would expect that you’ll see familiar concepts captured within the tag cloud and representative tweet content.

If you’d like to see the data cut another way, I’d encourage your feedback, or contact me to discuss how we could apply this form of discovery to the results of your next survey project.

mTAB Understands Qualitative Research Needs!

I am an administrative analyst at Productive Access, the creators of mTAB™. I hold a masters degree in Sociology, and used qualitative analysis to write my thesis.  With this background I understand the statistics behind survey data analysis and the use of cross-tabulation to analyze survey data, but neither technique is really my forte.

I had always considered mTAB™ a program that gives priority to quantitative data.  Generally marketing departments, and analysts, are looking to crunch the numbers of their demographics in order to fully understand the popularity of their products, so qualitative analysis isn’t in high demand. Some executives, however, recognize the importance and need for qualitative research.  Data tabulations can only go so far; as a means of truly understanding what data is saying, innovative analysts utilize qualitative analysis techniques using survey verbatim questions, otherwise known as unstructured data.

 

 

 

 

 

 

Ultimately quantitative and qualitative data work best as a team. For example a particular brand of car might not be selling well in certain demographics. Some may look at this quantitative finding and decide to scrap their model. This is where qualitative research really shines; looking into what is said by those unsatisfied consumers may highlight a product need that would have been otherwise overlooked. In sociology we were always made very aware of this phenomenon.

My professors always said, “Correlation does not equal causation”. This sentiment is true in all research. Correlations can be spurious and based on another factor. For example: rainbows seem to be positively correlated with a healthy garden. Seeing this data I may ask my employees to create a rainbow machine so my tomatoes will grow. You may think this sounds silly, but many marketing departments and analysts look at the correlations of data and don’t look into the other factors. Rainbows happen after it rains. Rain helps gardens grow. Rainbows have a spurious relationship to healthy gardens.

Luckily Productive Access understands the need for both quantitative and qualitative research, and has worked hard to incorporate a system that will help the mTAB™ user better understand the implications of their survey’s unstructured data: tag clouds.  I was extremely pleased when I saw this function added to our latest release of mTAB™. Now when an analyst wants to further understand their data they can look to their verbatim responses.

As a researcher I understand that qualitative data is really rather daunting; the researcher may have no idea where to start, and the paragraphs of responses are not easily sorted into crosstabs that show percentages. With the addition of the Tag Clouds in mTAB™ you now have an immediate indication of the most prevalent words used within the verbatim cells. When using tag clouds in mTAB™ a dialogue box appears showing the most commonly occurring words from the Verbatim Report and indicates the frequency of occurrence by size and boldness.  The largest and boldest being the most frequent. Now with one look you can tell what issue is most prevalent within the group you are analyzing.

There is a great webinar on the PAI web site that illustrates how to incorporate a qualitative analysis of unstructured survey data within the framework of qualitative cross-tabulation survey analysis.   This short video clearly illustrates how mTAB’s tag cloud feature benefits the analysis of surveys containing both structured and unstructured data.

I love the new Tag Clouds in mTAB™, and am happy to be with a company that persistently works to improve their program for all users.

If you would like to find out how the PAI team could help you combine the analysis of your survey structured and unstructured data, please register here for a no-obligation consultation.