Univariate K-Means Clustering vs. Fixed Cluster Boundaries

I am attempting to cluster stores based off their sales. I can either do:

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1. Univariate K-Means clustering by way of the Ckmeans.1d.dp package in R. This works perfectly fine, only 2 cons are figuring out the upper limit on K, and possibly explainability to the client.
2. Fixed cluster boundaries. In this case, I average the sales of all stores, and create boundaries like: 50% below average, 25% below average, 25% above average, 50% above average. This is …

client cluster clustering cons datascience explainability k-means package sales stores