A regional sales manager notices reps are spending significant time on top-scored leads that turn out to be shuttered businesses or contacts who have changed roles, while genuinely promising leads sit lower in the AI-ranked worklist. Investigation shows the scoring model is weighting firmographic fields, such as company size and industry, that have not been refreshed in over a year. What should the consultant recommend?
Select an answer to reveal the explanation.
Short Explanation
When top-ranked leads keep turning out to be dead ends, chase the data, not the ranking mechanism. Here the model is leaning on company size and industry fields that nobody has touched in over a year, so it is confidently ranking stale information as if it were current, and reps are paying the price by chasing businesses that no longer exist. Shifting more weight onto recent activity signals softens the effect a little, but the old, wrong firmographic data is still sitting there in the mix, quietly capable of throwing off the score in other ways. Asking reps to hand-verify every single lead before it even gets scored basically undoes the whole point of having an automated ranking system, since now a human has to touch every record anyway. And just moving the cutoff score up or down changes who shows up at the top of the list, but it does not make the underlying numbers any more accurate, so the same shuttered businesses could still float near the top. Refreshing the data itself, on a recurring schedule, is what actually fixes the source of the problem.
Full Explanation
The correct answer is A. Because the root cause is stale firmographic data feeding the scoring model, setting up a recurring enrichment process to keep those fields current directly addresses the problem, ensuring the model ranks leads based on accurate, up-to-date company and contact information. Option B is incorrect because shifting weight toward activity signals only dilutes the influence of the stale data rather than correcting it, leaving inaccurate firmographic information still baked into the score and still capable of misleading rankings in other ways. Option C is incorrect because requiring reps to manually re-verify every lead before scoring defeats the purpose of automated scoring and does not scale across a full pipeline of leads, reintroducing the manual burden the AI feature was meant to remove. Option D is incorrect because lowering the score threshold changes which leads surface at the top but does nothing to fix the underlying data driving those scores, so shuttered businesses and outdated contacts could still rank highly. Refreshing the data at its source is the fix that targets the actual cause of the misranking.