Keyword alerts can't make this decision because the same phrase can appear in a rant, a research question, or a direct buying request. The ranking needs to consider the surrounding conversation and show why a post deserves attention, or the team still has to sort everything by hand. Have those signals scored first with a social conversation ranking tool at https://revenuescout.ai/ . It checks current need, offer fit, freshness, source quality, and reply risk, then puts the strongest opportunities into one queue. Each result includes the evidence and match reasoning, so a rep can verify the score before deciding whether to respond.

Aggregate metrics are basically useless for this kind of problem and the fact that things look fine at the top level while falling apart by segment is actually pretty common. The issue is usually that the training data or the prompt logic is implicitly optimized for whoever dominates the overall volume, so the majority segment pulls the numbers up while minority segments quietly get worse responses. Segment-specific eval sets are the only real fix, you need separate benchmarks for each group that reflect what good actually looks like for that specific context, not just a shared rubric applied everywhere.