Currently, I am working on customer segmentation using their purchase data.
So, my data has below info for each customer
Based on the above linked posts I see that for clustering, we have to scale the variables if they are in different units etc.
But if I scale/normalize all of them to uniform scale, wouldn't I lose the information that actually differentiates the customers from one another? But I also understand that monetary value could construed as high weighted feature because they might go upto range of 100K or millions as well.
Let's assume that I normalized and my clustering returned 3 clusters. How do I answer below questions meaningfully?
q1) what is the average revenue from customers who are under
q2) what is the average recency (in days) for a customer from cluster 2?
q3) what is the average age of customer with us (tenure) under cluster 3?
Response to all the above question using normalized data wouldn't make sense because they all amight be in a unform scale mean 0, sd 1 etc. If I say average age in cluster 3 is 0.356, it wouldn't make sense to users.
So, I was wondering whether it is meaningful to do the below
a) cluster using normalized/scaled variables
b) Once clusters are identified, use
customer_id under each cluster to get the original variable value (from input dataframe before normalization) and make inference or interpret clusters? Is it okay to interpret clusters this way (and not using nornalized values)
c) I believe the pattern found in nornalized data is also applicable to original data (even though we disnt feed the original data representation to the model)
So, do you think it would allow me to answer my questions in a meaningful way
Is this how data scientists interpret clusters? they always have to link back to input dataframe?