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I have this below requirement. Please let me know, if it is possible to do in Alteryx designer as I am aware that it can be done in R studio. We have thousands of Items which are sold in retail stores all year round. We want to do an ABC classification on the items. So, that we know, which Items are most important and so on. Here is what we have currently?
•Sales history both qty and value of last 18 months
•Item hierarchy (Item, Class, sub category, Category)
We want to provide weightage also to the parameters.
Like Margin should have 50% weightage, sales unit 40%, sales value 10% weightage.
Finally, I should be able to modify the weightage in a cell and the whole report should reflect.
Like: Type 50 in weightage and enter, then data should change. Type 60 and enter, then data should change.
in general, cluster analysis is possible in Alteryx, you'll find K-Centroids cluster analysis in Predictive Grouping tools. As Alteryx uses R to perform cluster analysis, it's comparable to what you can do within R Studio (but easier to handle).
To apply weights to the parameters, the solution I can imagine is to scale values (maybe using z-score before handing over data to the K-Centroids Cluster Analysis tool) and multiply the scaled values with the weights you want to apply. I would recommend scaling anyway - revenue, units and margin (especially margin in %) are very different scaled, leaving this values unscaled would probably distort the results. Of course, changed weights would influence the results only after running cluster analysis again. I think, R has also no solution to apply weights differently.
Thanks for the reply. Here, I have multiple Items on which I want to perform the clustering. My doubt is: How do I calculate the Z score of multiple item's sales qty or sales value? In the online z score calculator, it is asking me to input the mean, SD and value. Is there any simpler way to create the Z score easily? Then I can go to Alteryx and see the tutorial on how to use this data to create clustering. Thanks again.