A sales operations manager wants relationship analytics to flag when a champion contact at a strategic account has gone quiet — specifically, when meeting frequency with that contact has dropped by more than half compared to the prior quarter, even though overall email volume with the account remains steady. Native relationship health scoring on the contact doesn't isolate meeting cadence from other interaction types. How should the manager enhance the scoring to catch this pattern?
Select an answer to reveal the explanation.
Short Explanation
The signal the manager cares about is buried inside a broader score, so the fix has to pull it back out. What actually works is writing a small piece of custom logic that specifically counts meetings with that contact quarter over quarter and raises a flag once the drop crosses the threshold, then showing that alongside the relationship data that's already there. Turning down the whole account's score threshold doesn't isolate anything, it just changes sensitivity for the entire account regardless of what's driving it. Leaning on the rep to self-report weekly reintroduces exactly the kind of inconsistent, easy-to-forget manual step that automated relationship tracking exists to replace. And stripping out email signals entirely doesn't compute the comparison either, it just throws away a data point without adding the calculation that's actually missing. A targeted calculation layered on top of the existing signals is what surfaces this specific pattern.
Full Explanation
The correct answer is A. Because relationship analytics does not isolate meeting cadence from other interaction types, the manager needs custom logic, built with Power Automate or a Dataverse calculation, that compares each contact's quarter-over-quarter meeting counts and raises a flag when the drop exceeds the defined threshold, surfacing that signal alongside the existing relationship data. Option B is incorrect because lowering the account-level threshold changes scoring for the whole account rather than isolating the one contact's cadence, and it would also flag or miss accounts for reasons unrelated to this specific champion. Option C is incorrect because relationship analytics is built from actual interaction data rather than rep self-reporting, and relying on manual weekly check-ins reintroduces the inconsistency and delay the manager is trying to engineer around. Option D is incorrect because removing email-based signals discards information relevant to overall account health and does not itself compute or compare meeting cadence; it merely narrows the existing signal set without adding the calculation the manager actually needs. Custom logic layered on top of the existing signals is what closes the specific gap described.