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Segmentation studies are expensive to execute and challenging to implement. Improve your ROI with an innovative solution, easier to use for your sales reps.
A standard deliverable from segmentation studies is a segment classification typing tool. However, it is often a challenge to get sales reps to use segmentation typing tools because of the requirement for all of the typing tool questions to be asked and answered.
We present Bayesian Typing Tools as an innovative solution to that problem. Bayesian Typing Tools produce segment predictions even if answers to some of the typing tool questions are not available. Bayesian Typing Tools are much easier for sales reps to use and make it much more likely that segmentation study results will actually be implemented by sales reps.
In this video the following points are presented and discussed by Steve Bell, AplusA's Chief Methodologist, including a live demo of standard and Bayesian typing tools in action:
- Role and importance of Segmentation Typing Tools
- How Segmentation Typing Tools conventionally work
- Rationale for Bayesian Typing Tools
- How a Bayesian Typing Tool works
- Summary and future directions
This session was originally presented at the Sawtooth Software European Conference in September 2020, By Steve Bell.
The question is to identify whether multiple underlying dimensions can be found to what is usually called the “importance” of attributes.
2 ways of measuring attribute importance are used in this presentation:
- First using pair comparison (“which of these 2 attributes is more important for your choice”)
- Then a MaxDiff exercise (“of these 5 attributes, which one is the most important, which is the least important”)
Steve reveals 2 dimensions of “importance” from an extensive set of data: based on an actual real-world survey conducted with 750 physicians and providing almost 14,000 pair comparisons from 18 rated attributes.
So through this presentation, you will get some tips on:
- How to write product attributes that allow fair comparisons
- What to do with attributes overlapping
- What is the effect of considering 2 possible dimensions: impact, and scope– or generality- of attributes?
- How to consider the difference in scope of attributes to draw the analysis, when one attribute is possibly a part of another
- How to possibly filter out the noise from differences in scopes of attributes
- How to calculate and present dissimilarity from pair comparisons
- How to apply multidimensional scaling (MDS) to the data
- How well can choice modeling interpretation predict pair comparison choices?
- How to draw a distance-based choice model (DBCM)
Finally, comparing the maps obtained through Multidimensional scaling vs. distance-based choice modeling and their relative performances to predict pairwise choices, Steve describes ways to estimate product attribute ratings even when the attributes have very different levels of scope (or generality).