R is one of the most powerful coding languages for analyzing data. It’s used by millions of people across the globe, and is free to…
R is one of the most powerful coding languages for analyzing data. It’s used by millions of people across the globe, and is free to…
Shapley Regression, also known as Shapley Value Regression, is the leading method for driver analysis. It calculates the importance of different predictors in explaining an…
Fully automated text analysis can, sometimes, do a great job. However, the gold standard for automatic categorization is to first get a human being to…
A basic problem with both Stated Willingness-to-Pay and the Price Sensitivity Meter is that they are asking people to nominate prices. The issue is that…
Pricing Knowledge/Awareness is our second technique used for pricing research studies. You can find our first technique, Price Salience, here. The basic idea of Price…
An alternative approach (from Stated Willingness-to-Pay) for asking people what they will pay for something is known as the Price Sensitivity Meter. This approach asks…
Displayr has a built-in Sample Size Description widget (under Insert > More > Data > Sample Size Description) that you can use to describe the…
In this post I describe how to quickly create a quad map in Displayr. The example uses a Shapley Regression to work out the relative…
The value equivalence line is a useful concept for setting pricing strategies in markets where products vary in terms of their overall levels of benefits…
This post explains the basics of computing willingness-to-pay (WTP) for product features in Displayr. Step 1: Estimate a choice model with a numeric price attribute…
You can check, in Displayr, who has accessed your documents and made changes to your account’s users. This post describes how to do just that.…
It’s easier than you think to use basic JavaScript to create new variables! The following worked example in Displayr shows you how to combine, split,…
When sharing reports, it’s often necessary to create multiple reports with a different filter on the data. For example, you need a hard copy report…
A driver analysis is used to highlight the key drivers of performance. Traditionally, it uses quantitative data, where the outcome variable is often satisfaction, likelihood…
5 Machine Learning Breakthroughs to Accurately Categorize Text! For the last 20 years, the survey research industry has waited with bated breath for text…
Manually coding text data into categories is one of the great pains of survey research. By contrast, many automatic text coding tools ease the pain…
Often we want to customize the look of tables. Perhaps you need to show the counts below the bars of a visualization. Perhaps you need…
In this post, I provide a walk through of how to use Displayr’s text analysis for multiple response verbatims. I focus on what we refer…
It can often be difficult and time-consuming to organize raw text data into meaningful insights. Manually coding even a single text question can take several…
Text data often refers to entities, such as people, organizations, or places. These entities can be automatically extracted from text data, and then used in…
Categorizing text data can be a time-consuming and expensive activity. In cases where time is short and budgets low, using automatic categorization of text data…
By adding a line to a column chart, you can add context to make your visualization more compelling and better display the relationship between two…
If you can export an SPSS file from your survey platform to use in your analysis, then Displayr will usually be able to do all…
This post shows how to use Displayr to replicate the standard SPSS functions of Transform > Recode into Same Variables and Transform > Recode into…
Ready to save an enormous of time when sifting through thousands of crosstabs? This post describes which buttons to push in Displayr in order to…
Videos can be embedded into Displayr documents with a few lines of code. This is particular useful for showing videos of advertisements and vox pops.…
Sometimes a variable in a data set will be missing a category (e.g., maybe nobody said “not aware” for Coke). If using a rich data…