Does anyone actually know what a bad skip trace return rate looks like before you waste money on a list

The comparison point is almost always internal, which is why you never see a clean industry floor. Most operators are benchmarking against their own prior runs on the same list type from the same source, so a 30% hit rate on a tax-delinquent list from provider A tells you nothing reliable about a 30% hit rate on an absentee-owner list from provider B. The data category and the provider's matching methodology both matter, and those two variables make cross-operator comparisons nearly meaningless without knowing both.

That said, there is a rough working logic that comes up in operator circles. On a cold list with no prior filtering, hit rates in the high teens to low twenties are generally treated as a data quality problem, and the fix is usually provider-side rather than dialing harder. Somewhere in the 30s is where most experienced operators seem to stay in the game but run a parallel test pull to see if a different source or a smaller, tighter segment performs better. Below 15% on a warm or filtered list, something is structurally broken, either the list is stale, the criteria are too broad, or the provider's skip logic is poor.

The assumption doing the most work in any skip trace evaluation is list age. A list that is six months old versus eighteen months old can swing hit rates by 15 points or more on mobile numbers, because people change numbers and move. If you are not dating your pulls and tracking hit rate against pull age, the rate number alone is nearly useless as a signal.

The cost structure you are probing is real but it compounds past the skip trace step. A high hit rate on bad contact data just means you dial more people who tell you they are not the owner, which burns caller time at a higher cost per qualified conversation than a lower-hit-rate list with tighter criteria.

What list type and data category are you looking at, and are you pulling fresh or working with existing data?