A consultant validated a client's predictive opportunity scoring model eighteen months ago before rollout, confirming enough historical closed opportunities existed to train a reliable model. Since then, a new competitor has entered the client's market and the sales team's typical objections and win patterns have visibly shifted, but no one has looked at the model again. A sales manager asks whether the original validation still applies. What should the consultant recommend?
Select an answer to reveal the explanation.
Short Explanation
A predictive model is only as good as the patterns it learned, and those patterns have a shelf life. Passing the check before rollout confirmed there was enough history to learn from, it says nothing about whether the world still looks the way it did back then. When a new competitor shows up and starts changing what objections and win patterns look like, the market is quietly moving out from under the model's assumptions, and nobody is watching for it. The fix is not to panic and rip the whole thing out the moment something changes, a shift in the landscape is a reason to check in and retrain if the numbers say so, not a reason to declare the tool broken. It is also not something reps should patch around with manual guesses, since that leaves the actual model untouched for everyone else relying on it. Treat the original review as a starting point, not a permanent stamp of approval, and build a habit of checking predictions against what really happened.
Full Explanation
The correct answer is C. A predictive opportunity scoring model is trained on historical patterns, and those patterns can go stale as the market changes; a new competitor shifting typical objections and win rates is exactly the kind of drift a one-time pre-rollout validation cannot catch on its own. The right response is ongoing monitoring, comparing predicted scores against actual outcomes over time, and retraining or adjusting the model when the data shows its predictions no longer track reality, rather than treating the original validation as a permanent guarantee. Option A is incorrect because passing a data-volume check before rollout only confirms the model had enough history to train on at that point; it says nothing about whether the patterns it learned still hold eighteen months and a market shift later. Option B is incorrect because it overreacts: a shifting market is a reason to monitor and refresh a model, not to abandon a working tool entirely, especially without evidence the model's predictions have actually degraded. Option D is incorrect because it pushes the fix onto individual reps making ad hoc manual adjustments to scores, which creates inconsistency across the team and does nothing to correct the underlying model everyone else still relies on. Ongoing governance, not a single validation, is what keeps a predictive model trustworthy.