Sawtooth is offering a new and innovative 8-hour in-person workshop on market segmentation this summer! This is the second day of a 2-day in-person workshop, just south of Salt Lake City.
To learn more or enroll, visit: https://lnkd.in/gQ3HDNya
MARKET SEGMENTATION BOOTCAMP:
Segmentation can be the most frustrating kind of study marketing researchers face. Much of the difficulty is inherent in the complexity of what we’re trying to do with segmentation. But some is difficulty we make for ourselves by not having a clear process that takes into account the foreseeable complexities or by using analysis methods that reduce our chance of finding a good segmentation solution.
This session begins with a general description of what segmentation seeks to accomplish and a general process that textbooks advise. We’ll then cover a number of hard truths about the segmentation enterprise that help explain why so many segmentation studies fail. In addition, we’ll identify a number of smaller pitfalls that a successful segmentation process will avoid.
Next, we’ll show a process that takes the hard truths above into account and that also avoids the smaller pitfalls. We’ll start with a new way to conceptualize different kinds of segmentation, because some traditional methods are mis-aligned with the goals of some types of segmentation (and some are mis-aligned for any use in segmentation whatsoever). We’ll describe some newer methods, many borrowed from the machine learning literature, that can help us navigate the hard truths and the pitfalls, to increase our chances of successful segmentation. We’ll also provide advice about statistical software packages for running segmentation and we’ll provide R code for running the analyses we describe.
The bulk of the time we’ll spend demonstrating different kinds of analysis using different kinds of software:
Variable selection using R software to run:
-Unsupervised random forests
-An automatic variables selection method for metric data called ClustVarSel
-An automatic variable selection method for mixed scale data called VarSelLCM
-Supervised random forest for predictive segmentation
Segment generation:
-Needs-based segmentation: latent class MNL in Sawtooth Software
-Supervised segmentation: Tree-based methods in R
-Unsupervised segmentation
-Cluster ensemble analysis in Sawtooth Software’s CCEA package
-Model-based clustering using Latent Gold software
Typing tool creation:
-MaxDiff typing tool for needs-based segmentation
-Discriminant analysis in SPSS
-MNL in SPSS or R
-Tree-based classification in R
-Supervised random forest for segment assignment
-SVM for segment assignment
Finally, we’ll end with a decision flow chart showing the decision points and options to guide you through our recommended segmentation process.
#mrx #quantux #sawtooth #marketsegmentation
