Six months after enabling predictive opportunity scoring, a sales operations analyst notices that most opportunities imported during a recent CRM migration all display an identical predictive score, regardless of deal size, industry, or stage. Opportunities created natively in Dynamics 365 since go-live show varied, sensible scores that track with deal characteristics. What is the most likely explanation?
Select an answer to reveal the explanation.
Short Explanation
When a whole batch of records shows the exact same score while everything created normally looks varied and reasonable, that pattern almost always points to something about how that batch got its data, not some special rule about scoring or environments. Migrations are notorious for leaving gaps, activity history, certain relationship fields, engagement signals, things that do not always map cleanly from an old system into the new one. Feed the model records with those gaps and it cannot tell one from another, so it lands on the same safe fallback number across the board, which is exactly the flat pattern being described. It is worth setting aside explanations that do not fit the facts given, like some age-based rule that would kick in after six months, or the idea that the records are secretly sitting in a completely different environment, since the analyst is clearly viewing both sets side by side in the same place. And no one needs to hand re-save thousands of records one at a time, that is not how the scoring engine refreshes its output. The real fix is cleaning up the migrated data itself.
Full Explanation
The correct answer is C. When records are brought in through a migration, fields the model depends on as predictive signals, such as activity history, industry, or engagement data, often do not carry over cleanly, leaving gaps the model cannot reason about. Faced with missing signal data, the model produces a uniform fallback result rather than a differentiated score, which matches exactly what the analyst observed: natively created records with complete data show varied, sensible scores, while migrated records with data gaps do not. Option A is incorrect because there is no rule that ages opportunities out of individualized scoring after six months; scoring behavior is driven by data quality and model configuration, not record age. Option B is incorrect because the scenario describes the analyst comparing both sets of opportunities within what is clearly the same working environment; if they were in separate environments, the analyst would not be able to view and compare them side by side in the first place. Option D is incorrect because it invents a manual re-save requirement that is not how the scoring engine recalculates values; scores update based on data and scheduled processing, not a manual per-record save action. Data completeness at the point of migration is the real driver of the pattern described.