A sales leader wants whitespace analysis to include product usage data drawn from an ERP system, which relationship analytics does not ingest natively. Which approach extends the platform to support this cross-sell analysis?
Select an answer to reveal the explanation.
Short Explanation
The obstacle in this scenario is a data boundary: the built-in analysis tool only knows how to look at information that already lives inside the platform, and product usage history sits somewhere else entirely, in the ERP system. Simply switching the built-in feature on and waiting does not change what it is capable of reading — it was never wired to reach into that other system, and time alone will not fix that. Reaching for a lead-scoring tool does not fit either, because that tool judges the promise of a new lead, not the cross-sell potential hiding inside an account that already has a track record. Asking reps to jot down usage details in a free-text box produces scattered notes that cannot be compared, filtered, or analyzed the way real cross-sell whitespace analysis requires. What actually closes the gap is building a report that pulls both data sets together — the account details from one side and the usage history from the other — into a single place the sales team already visits every day.
Full Explanation
The correct answer is A. Relationship analytics evaluates interaction data already captured inside Dataverse, so it has no path to ingest product usage history stored in a separate ERP system; combining the two data sets requires a reporting layer built for that purpose, which a Power BI report can provide by blending Dataverse account records with ERP usage data and then embedding the result directly in the Sales Hub app where the sales team already works. Option B is incorrect because simply enabling the standard relationship analytics feature does not add any new data connections; it will keep analyzing only the sources it already supports, regardless of how long the team waits. Option C is incorrect because the Sales Qualification Agent is built to assess and score individual leads for qualification purposes, not to identify cross-sell whitespace across an existing account's product footprint. Option D is incorrect because a free-text field filled in manually by reps produces inconsistent, unstructured notes rather than the kind of structured cross-source analysis the leader is asking for, and it does not scale across the account base.