IronBrand Overview: A Guide to Improving Health System Performance
A brand is an amalgam of thoughts, feelings, associations, experiences, and memories that each person has from previous encounters with the brand. Further, consumers have many ways to interact with myriad brand choices in today’s marketplace.
Ironwood’s IronBrand Brand Equity model addresses these complex market dynamics and provides a foundation for brand-building that reflects the many ways consumers interact with today’s brand choices.
Artificial Intelligence (AI) and Machine Learning in Market Research
There continues to be a great deal of hype surrounding the use of artificial intelligence (AI) and machine learning in market research. In this white paper, we explore two of the most commonly used machine learning techniques used by market researchers: cluster analysis and decision tree analysis (and use real-world examples to help).
Graphic Display of Data: Box Plots
Box plots, also known as box and whisker plots, have been around for over 50 years. Many of today’s modern researchers don’t include box plots in their analytical toolkit even though the box plot chart provides a valuable way to graphically view and summarize large amounts of data. They can be particularly useful when trying to evaluate the shape and variability of response distributions from several groups of respondents or on several different products. In this white paper we will explore the uses of box plots along with their strengths and weaknesses compared to other data summarization tools.
What’s Driving Your Customers’ Satisfaction?
FINDING THE STORY USING ADVANCED ANALYTICS
There are a number of statistical tools available to today’s researchers to help them learn about the drivers of general attitudinal outcomes such as overall satisfaction. This type of key driver analysis is often done using linear regression. In this White Paper, we’ll explore Attributable Effects, a different kind of analytics tool designed to yield even more actionable information about your customers.
Minimizing Losses When Choosing Confidence Levels
Frequently researchers become set in their ways when it comes to their approach to research. They will use the same number of observations and the same statistical tests repeatedly. This rigidity is especially present when it comes to choosing a level of confidence from statistical testing. And those confidence levels can impact the bottom line… which we’ll explore in this white paper.