Robin Hood hash works well with high load factors ~95% because it balances the average lookup with a fair distribution of buckets. That makes it ideal when you don't want to waste memory on bloated tables.
I think the 0.95 figure for Robin Hood hash tables is rather optimistic. Robin Hood helps in a few ways: it allows for early termination of failed lookups, and it reduces probe-length variance (thereby making the performance of any one hash table operation more predictable). However, it does not reduce the average number of probes needed to perform a successful lookup. The hash-table benchmarks I published last year (https://jacksonallan.github.io/c_cpp_hash_tables_benchmark/#...), which test various hash tables in the 0.44-0.875 load-factor range, show that the performance of Robin Hood tables ("ankerl" and "tsl" in the article) deteriorates rapidly as the load factor climbs high, just like conventional linear- and quadratic-probing tables. This is in contrast to the SIMD and hybrid open-addressing/separate-chaining tables, whose performance is much more stable across the whole load-factor range.
