A sales operations lead configured the AI-driven lead scoring model with a low qualification threshold so that most inbound leads are marked 'hot' and routed to sellers for immediate follow-up. After a month, sellers report that a large share of their 'hot' queue turns out to be unqualified, and they are spending more time filtering leads manually than before scoring was introduced. A consultant is asked to adjust the configuration to fix this without disabling scoring. What should the consultant recommend?
Select an answer to reveal the explanation.
Short Explanation
The problem here isn't that scoring is a bad idea, it's that the bar for calling something 'hot' was set too low, so almost everything gets waved through and sellers end up doing the filtering by hand anyway, which defeats the purpose. The fix is to tighten the cutoff. Yes, that means a few genuinely promising leads will now land just under the line and get flagged as warm instead, but the payoff is that when something does show up in the hot queue, sellers can trust it's actually worth dropping what they're doing for. Feeding the model more historical data sounds appealing, but it doesn't touch the actual issue, which is where the cutoff line is drawn, so that alone won't shrink the flood. Going the other direction and loosening the bar even further only makes the queue bigger and noisier. And scrapping scoring altogether throws out a tool that was working, just poorly calibrated, in favor of the exact manual triage sellers were already unhappy about doing. Calibration, not abandonment, is the move.
Full Explanation
The correct answer is C. Raising the qualification threshold trades some recall for precision: fewer leads will clear the bar as 'hot,' but a larger share of those that do will genuinely be worth immediate seller attention, which directly addresses sellers spending time filtering unqualified leads out of an oversized queue. Option A is incorrect because adding training volume without changing the cutoff does not change which score range counts as 'hot'; a model trained on more data can still assign a wide range of leads above a low threshold, leaving the same filtering burden. Option B is incorrect because removing scoring entirely discards the benefit of prioritization altogether and returns sellers to first-in-first-out handling, which is a regression rather than a fix for a threshold that is merely miscalibrated. Option D is incorrect because lowering the threshold further increases the size of the 'hot' pool and would make the unqualified-lead problem worse, not better, since more low-scoring leads would be admitted into the queue sellers are already struggling to filter.