Fundamentally Speaking: Back To The Basics in Marketing Research

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.” During a recent NY / Philadelphia MRA conference, we heard repeatedly that it is not only the marketing research supplier facing these issues but also client companies and their end users. In the real world, when facing such aforementioned constraints, what might help in delivering on objectives while continuing to provide credible information?

Throughout many aspects of life, we find success by returning to fundamentals. Although getting back to basics sounds obvious, it is often overlooked. When a baseball player experiences a hitting slump, what does he do? He works with a hitting coach to determine if his fundamentals are correct (i.e. stance, swing, mental approach, etc.) The financial markets are no different. Often we read that the financial market needs to turn to fundamentals in order to back on track or grow. CEO’s of large corporations often mention that their respective companies have solid fundamentals. Solid fundamentals translate into success.

In marketing research, returning to fundamentals and applying superior practices can lead to efficiency. Instead of rushing to find answers, we should not lose sight of basics such as what is it we want to learn. The “how we go about it” ought to come later. Simply obtaining information does not serve stakeholders competently.

It is very tempting and easy in the digital world to go online and get information. Digital is built for speed. However, the old adage “garbage in/garbage out” has never been more valid. More information should never be our goal but, instead, meaningful information.Fundamentals in research tie directly back to getting it right the first time. Today there are many resources from which to garner information. Whether we are conducting primary or secondary research, qualitative or quantitative, there are fundamentals that must be deployed in order to capture findings that meet our goals. “Back to the basics” comes down to applying the right resource against what we want to learn. The mistake of placing the cart before the horse is not new.

Successful solutions are not solely about research technique but in applying optimal methodology to yield credible outcomes. Our challenge is not to be induced by the speed associated with the latest and/or so called greatest but to leverage wide ranging knowledge in order to get it right. Poor designs associated with problem definition, sampling frame, questionnaire development, execution or analysis will guarantee dire results. Starting with fundamentals gives us a foundation from which to build a successful project.

[Editor's Note: This post originally appeared on Steve Levine's Blog, and is syndicated here with permission.]

QR Codes Still Kicking

A few months ago, we talked about how QR codes were poised to change the market research industry. Or perhaps, how the time for QR codes had already come and gone. Well, whether they’re the revolutionary technology that so many have claimed them to be or not, they’re definitely still alive and kicking!

A new study from comScore shows that 14 million people in the United States – some 6.2% of the total mobile audience – scanned a QR code in the month of June. The report also showed that QR code users are particularly popular among males (60.5%), between the ages of 18 to 34 (53.4%) who have a household income in excess of $100,000 (36.1%).

Where are these QR codes found? Magazines and newspapers top the list (49.4%), followed by product packaging, (34.3%). The preferred place to scan was at home (58%) and then retail stores (39.4%).

So what’s your verdict? Have you used (or do you plan to use) QR codes as part of a marketing or research campaign? Have you had any success with them, or has their time already come and gone? We want to hear your thoughts, here in the comments or via Twitter (@researchaccess).

Why Size Counts (And Why Shorter is Better)

Jeffrey Henning (of the renowned Voice of Vovici blog) brought two links to my attention this morning via Twitter, and I wanted to be sure you all caught them as well. The topic of both pieces was size – specifically, why the size (or length, if you rather) of surveys really matters, and equally importantly, how to do something about it.

The first link comes from Michaela Mora at Relevant Insights. (We’ve mentioned Michaela’s content before; she provides really great content on a variety of market research topics.) The article is “Why We Need to Avoid Longer Surveys,” and in it, Michaela makes (and provides supporting data) for a very important point: Long surveys often mean bad data.
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Poor question design means questionable results: A tale of a confusing scale

I saw the oddest question in a survey the other day. The question itself wasn’t that odd, but the options for responses were very strange to me.

* 1 – Not at all Satisfied
* 2 – Not at all Satisfied
* 3 – Not at all Satisfied
* 4 – Not at all Satisfied
* 5 – Not at all Satisfied
* 6 – Not at all Satisfied
* 7 – Somewhat Satisfied
* 8 – Somewhat Satisfied
* 9 – Highly Satisfied
* 10 – Highly Satisfied

What’s this all about?  As a survey taker I’m confused.  The question has a 10 point scale, but why does every numeric point have text (anchors). What’s the difference between 1, 2, 3, 4, 5 and 6 that all have the same anchoring text?   Don’t they care about the difference between 3 and 5?  Oh, I get it, this is really a 3 point scale disguised as a 10 point scale.

With these and other variations on the theme of “what were the survey authors thinking?”  on my mind I talked to a representative from the sponsoring company, AOTMP.  I was told that the question design was well-thought out and appropriate, being modeled on the well-known Net Promoter Score.   Well of course it is  – like an apple is based on an orange (both grow on trees).  But not really:

1. The Net Promoter question is for Recommendation, not Satisfaction.  There were a couple of other similar questions in the short survey, but nothing about Recommendation. Frederick Reichheld’s contention is that recommendation is the important measure and also incorporates satisfaction; you won’t recommend unless you are satisfied.
2. The NPS question uses descriptive text only at the end points (Extremely Unlikely to Recommend and Extremely Likely to Recommend).  It is part of the methodology to avoid text anywhere in the middle in order to give the survey taker the maximum flexibility.  That’s consistent with survey best practices.
3. The original NPS scale is from 0 to 10, not 1 to 10.  Maybe that’s a small point, although the 0 to 10 scale does allow for a midpoint which was part of the the NPS philosophy.

Other than the fact that this survey question isn’t NPS, what’s the big deal?  Well, this pseudo 10 point scale really doesn’t work.  The survey taker is likely to be confused about whether there is any difference between “3, Not at all Satisfied” and “4, Not at all Satisfied”. Perhaps the intention was to make it easier for survey takers, but either they’ll take more time worrying about the meaning, or just give an unthinking answer, and the survey administrator has no way of knowing.  Why not just use the 3 point scale instead?  I suppose you could, but then it would be even less like NPS. Personally, I like the longer scale for NPS.  I don’t use NPS on its own very much, but the ability to combine with other satisfaction measures with longer scales (Overall Satisfaction and Likelihood to Reuse) means that I’ve got the option of doing more powerful analysis as well as the simple NPS.  More importantly, I don’t have to try to persuade a client to stop using NPS as long as I include other questions using the same scale.  Ideally, I’d prefer to use a 7 or 5 point scale instead, but 10 or 11 points works fine – as long as only the end-points are anchored. For more on combining Net Promoter with other questions for more powerful analysis, check out “Profiting from customer satisfaction and loyalty research”

There’s no justification for this type of scale in my opinion.  If you disagree, please make a comment or send me a note.   If you want to use a scale with every point textually anchored, use the Likert scale with every point identified (but no numbers). Including both numbers and too many anchors will make the survey takers scratch their heads – not the goal for a good survey.

Perhaps the people who created this survey had read economist J.K. Galbraith’s comment without realizing it was sarcastic.- “It is a far, far better thing to have a firm anchor in nonsense than to put out on the troubled seas of thought.”