Every Picture Tells a Story

Social_Media_ReportIn the words of Rod Stewart – every picture tells a story. At MSI we’re constantly bantering about social media, which sites will make it, which ones will die a slow death. So we decided to take a deeper look at a variety of social and technology trends in 2013. And we’re going to tell that story with pictures. Our first foray was a look at the social media sites with the largest followings. And in order to tell the story, we used a tried but true method of having consumers associate “faces” of consumers with the sites. A total of 10 pictures were used to get out the faces and personality of the major social media sites.

And what we found may surprise you…
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Pretesting…a lost art?

Feedback_pretesting

There was a time when collecting quantitative data, more often than not, involved a conversation. And the conversation was a game where the respondent seemed to hold all of the cards. A lengthy interview, a poorly worded question, a missing response option – all represented excuses for the respondent to dismiss you by hanging up or otherwise ending the interview.

Can you tell I was an interviewer?

At that time, phone pretests often involved a painful process of listening to hours of rejections and hang-ups…but the result was invariably worth the price – a survey that more accurately collected the intended data.
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Big Data Scientists – Fact or Fiction An Interview with Dr. Mark Hayward, University of Texas

Big_Data_MSIRecently I have read a number of articles about Big Data Scientists. Actually, they have been about Scientists, Analysts, Researchers, etc. Nobody knows what to call them, so I’ll use the term “Scientists.”

And similarly, no one seems to agree on where they come from (or where they should come from). The arguments range from IT, Business Analysts and Statisticians to Social Scientists. As I read these debates, my gut reaction was to believe the best data scientists will come from the social sciences.
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Do you ‘Like’ it?

While I’ve had a Facebook account for a while, it’s only recently that I’ve started to pay a bit more attention to some of the behaviors of my friends and family. (That’s what happens when your youngest goes off to school and your use of Facebook picks up to see what he’s up to – or to stalk him, depending on your perspective.)

One area that I’ve questioned is the behavior to “Like” a business or product or famous personality. When you do, your “Like” is then advertised to your network. But why?
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Infographics: The Art of Visualizing Research

A few years back, I was invited to attend a webinar designed to help us think about how to express our study results in a more visually appealing manner than the usual bar, line and pie charts and endless tables filled with numbers that one typically sees in a market research report. It sounded like a great idea until I read the synopsis, which made some grand statement about visually displaying data without showing any numbers. At the time I thought ‘Are they crazy? No numbers? They do realize that we need to defend our insights with the actual numbers revealed by the study, don’t they?’
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How Big is “Big Data”

Right now Big Data is REALLY big – HUGE even (don’t you hate that word). Not a day goes by without someone posting a blog or writing an article on the value of Big Data.  And why shouldn’t they? The reality is that all of our clients are sitting on huge amounts of information about their customers and prospects – yes, terabytes and terabytes as most are fond of saying. The value of this information is immeasurable. And when we can derive actionable insights from this data the return is immense. The data itself is free (although sometimes integration of multiple databases can be a challenge) therefore insights gleaned from this data can have a major  impact on our product development and customer experience initiatives without requiring significant financial resources (OK, I realize that the advanced analytics aren’t cheap, but they are relatively inexpensive in the grand scheme of things).

But is Big Data really big enough to drive truly actionable market insights by itself? 
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Back to Basics: Doing More with Less – Stakeholder Engagement

In marketing research, we are frequently faced with getting answers faster and for less investment. In the current business climate, we have become all too familiar with the term “Doing more with less.” Along those lines, buyers of marketing research frequently face situations where there are fewer human resources available or less budget, yet the demands of stakeholders remain unchanged. When confronting such resource-restricted situations, one becomes tempted to rely on silver-bullet solutions in order to meet objectives. However, in life, there are rarely quick fixes or magic bullets that adequately serve our needs completely. So what does one do to ensure that we are delivering on objectives in spite of a limited pool of resources?
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Archetypal Analysis: Segmenting on the Extremes

So, I’m one to generally heed the sage advice to not talk about religion or politics with friends and family (though I sometimes cannot help myself :)).  But if you’re like me, you’ve likely been forced to at least think about or possibly even divulge your political affiliation during the current election season here in the U.S.

The simple question we’re asked is:  are you Conservative, Liberal, or Moderate?
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Marketing Research: Are We Experiencing Evolution or Revolution?

As defined by Webster’s Dictionary, evolution is a process of continuous change. Webster’s Dictionary defines revolution as sudden, radical or complete change. In other words, evolution occurs gradually over a period of time, while revolution is immediate. Within the scope of modern-day marketing research, we often read that given today’s digital age, we are experiencing a revolution. Considering the current technological times, which would apply more accurately – evolution or revolution?

The question of evolution versus revolution applies not only to marketing research, but also to everyday behavior. In order to stay concise, let’s take three examples:
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