How the Kano Model Tells Us New York’s Ban on Large Sodas Won’t Work

Date: June 28, 2012 | Shawn Herbig | News | Comments Off on How the Kano Model Tells Us New York’s Ban on Large Sodas Won’t Work

As researchers, we don’t like to just make a knee-jerk prediction that something is going to fail. After all, that’s what research is for. To determine attitudes toward a particular idea or proposal, and see whether enough people support it to make it viable.

But we have enough experience and knowledge to be reasonably certain that New York City Mayor Michael Bloomberg’s proposal to eliminate soda sizes larger than 16 ounces is doomed to fail. And we can use the Kano Model, plus our own attitudes toward food, to figure it out. Let’s start with the Kano Model.

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You May Not Be Measuring Customer Satisfaction the Right Way

Date: June 21, 2012 | Shawn Herbig | News | Comments Off on You May Not Be Measuring Customer Satisfaction the Right Way

Measuring customer satisfaction is a valid exercise, but if you take on the task, it absolutely must be done the right way. There’s a lot of junk science out there that promises to give you an insight into what your customers are thinking, but the reality is, if you or your research company is not using the right survey and research methods, the results you get back can, at best, be ineffective, and, at worst, will send you in the wrong direction causing you to waste a lot of time and money fixing issues that are not important.

One can argue that fixing any problem is a good step in the right direction but in business successful companies learn quickly how to focus their priorities on the issues that matter most. The role of research is to identify the real issues that – when fixed – will help move a company forward.

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Using Text Analytics as a Research Tool

Date: February 14, 2012 | Shawn Herbig | News | Comments Off on Using Text Analytics as a Research Tool

Imagine you are handed every letter to the editor to the New York Times from 1984 through 2011, and you are asked to find every reference to the word “immigration,” take a count, and determine an underlying sentiment or pattern of thought regarding the term.

And you have 12 minutes to do it.

Yeah, right.

But just like it has revolutionized every industry, technology has provided the automated solutions and tools that can do just that—and in more like 12 seconds—with text analytics.

Basically this tool takes large bodies of text to find not just counts of terms, but through creative key term combinations and simultaneous searches, the sentiment behind the terms can be measured and evaluated as well.

This works great for all the social network activity out there right now — and which is only going to grow over time — because all the thoughts, feelings, observations, etc. that are posted on the Internet are all in the format of searchable text. New data is generated every day, in enormous amounts, and it holds rich, measurable data that can help companies keep a pulse on their reputation and the positive or negative sentiment that is out there regarding their products and services.

Text analytics can also be used internally to measure employee satisfaction, management effectiveness, reactions to change, alongside surveys and responses, to discover anything that needs discovered. The possibilities are truly endless.

We’ve done a lot of text analysis for our clients (and sometimes for a little fun), and it’s always interesting to see, not only how attitudes change around a certain topic or theme, but even how language changes in general. We know some grammar geeks and linguists who love doing text analysis to see what has happened to the language in the last 10, 20, or even 100 years.

A New Development in Text Analytics

An interesting new development in this already relatively new solution is the anonymous versus non-anonymous reaction, and what people say and how they say them if they believe their comments are anonymous, like on newspaper websites.

Many news sites and blog sites are now requiring people to login to the site as members or use their Facebook profiles to be able to make comments on articles and posts. The interesting aspect here is how the tone, positive or negative, aggressiveness, and civility in comments changes when the individual knows that his or her identity will be known when commenting. This is another layer of text analytics that can open up whole worlds of research data…and will.

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