PAI Blog

What do airlines, large banks and telecom corporations have in common? They are among the least-liked companies in America.  How do we know? The American Customer Satisfaction Index (ACSI) tells us so. It’s the only uniform national measure of satisfaction with goods and services across a representative spectrum of industries and the public sector. The ACSI utilizes patented methodology to identify factors driving customer response and applies a formula to determine the cause-and-effect relationship between those factors and satisfaction, brand loyalty and overall financial health of a company. 

The world seems so much bigger today than it ever was.  According to reports, more than 1.2 zettabytes of digital information was created in 2010. The availability and importance of data is increasing at staggering rates; yet, at the same time, it costs billions of dollars to control. Companies that have placed little investment into data analysis resources struggle with data overload--unable to take advantage of the information available.

Big Data could mean Big Bucks for the companies that know how to gather it and use it. However, since the discovery that big data can help drive up revenue the number of organizations that actually use it are few and far between.

Improving customer experience has been reported as one of the top priorities for businesses in 2012. Some companies are even adding a new C-level position to their rosters, Chief Customer Officer (CCO).

Today we're going to do some thinking and learn about statistical demand analysis.  Why is statistical demand analysis important? Because estimating product demand is essential to reduce the risks inherent with pricing, production and inventory decisions. A failure to accurately estimate demand can lead to over or under production, and if priced incorrectly can negatively affect profits.

While some may have heard of the technique “factor analysis” many remain unclear about exactly what it is. How does factor analysis figure into decision making? How is it applied? What is it used for?  What are “latent factors”? While these terms may sound complicated or otherwise cumbersome the fact is that the ideas behind factor analysis are rather straightforward.

Welcome back to Making Molehills out of Mountains University. For years data analytics have been my passion. I have spent years looking at human behavior and applying statistical analysis techniques to answer two primary business questions every CEO has, “Should I do X” and “If I do X what will happen?”  There is a third question they often ask, “I did X, what happened? It was not what I expected.” But that’s usually asked when something like New Coke flops, uh, I mean, doesn’t meet expectations.

Welcome to the Making Molehills out of Mountains University (MMoM U) Market Research Data Analysis 101 or MARDA 1 as we like to call it in the halls of academia.  Today we discuss the four different types of scales used in measuring behavior.  Open your books and let's get started...

In an earlier post we talked about the difference between graphics used for visualization of data points and graphics used for presentation.  We concluded that the point of an analyst's effort when analyzing survey data was to communicate the results to busy decision makers in a format they could understand.