The second part in a three-part video series about predictive modeling. Kevin addresses the Confusion Matrix, and some of the common mistakes made at this stage.
Predictive modeling is hard. Thankfully our Chief Analytics Officer Kevin Mabe is here to help.
Conjoint analysis is the best way to get the most out of bundles. How does a team collaborate with this powerful tool to get the most out of their bundling strategy?
In a previous post, here, we introduced conjoint - its background, uses, and values. In this mini-post, we’ll focus on two specific industries we work with extensively - insurance and restaurant providers. What do these seemingly unrelated industries have in common? Bundles.
In our ongoing video blog series, Kevin tackles the basics and not-so-basics of predictive modeling.
All facets of business can benefit from predictive modeling. However, getting that analysis correct is challenging. Most don't take the time to do it correctly, or in some cases haven't even been shown the proper methodology.
Our new podcast will bridge the gap from our entry-level blog posts to the deeper analytical work of the "Hard Math with Kevin" video series. We'll keep the format generally to 15 minutes or less, so perfect for the commute - and it is also available to download via SoundCloud.
What can I charge? What should I charge? What will happen if I make a price change?
Every business looks at their competition to get a sense of what the ‘right’ price is for their product. But you aren’t your competition and that approach may miss important context from customers that goes deeper in their ability and willingness to pay for what makes your product different from your competition.
It also used to be expensive. Corus is changing that.
In the toolbox of the market researcher, few things are as powerful as a well done conjoint analysis. In this article, we will dive into the why and the how of this particular market research tool.
Cue the Jaws music...
Univariate Analysis is a huge part of market research, and survey analysis in particular. In this episode Kevin Mabe, our Chief Analytics officer, goes through an example dataset and then the analysis afterwords, and shows that once again first impressions are not always right. Enjoy!
In the latest episode of our ongoing series "Hard Math with Kevin" - our Chief Analytics Officer Kevin Mabe goes through a forecast using our sample time series analysis, and how the addition of a potentially statistically significant variable can change the overall forecast.
In the latest of our series with Chief Analytics Officer Kevin Mabe, we walk through why a time series analysis is important, and how trends can be evaluated.