diff --git a/_episodes_rmd/07-hierarchical.Rmd b/_episodes_rmd/07-hierarchical.Rmd
index 1cc445cb..16f44d0e 100644
--- a/_episodes_rmd/07-hierarchical.Rmd
+++ b/_episodes_rmd/07-hierarchical.Rmd
@@ -264,7 +264,7 @@ dendrogram showing how the data is partitioned into clusters. But how do we inte
 > Use `hclust()` to implement hierarchical clustering using the
 > distance matrix `dist_m` and 
 > the `complete` linkage method and plot the results as a dendrogram using
-> `plot()`.
+> `plot()`. Why is hierarchical clustering and the resulting dendrogram useful for performing clustering this case?
 >
 > > ## Solution:
 > >
@@ -272,6 +272,9 @@ dendrogram showing how the data is partitioned into clusters. But how do we inte
 > > clust <- hclust(dist_m, method = "complete")
 > > plot(clust)
 > > ```
+> > Hierarchical clustering is particularly useful (compared to K-means) when we do not know the number of clusters 
+> > before we perform clustering. It is useful in this case since we have assumed we do not already know what a suitable 
+> > number of clusters may be.
 > {: .solution}
 {: .challenge}