Question 1

What Chorus data can Polytomic sync?

Accepted Answer

Engagements, conversations, emails, scorecards, and users. Engagements are the meeting and call records and carry participants, summaries, action items, disposition data, and tracker matches. Conversations are fetched for meeting engagements and add recording details, speaker clusters, and the transcript itself. There are no standalone transcript, participant, or tracker objects; those come through nested on the records that own them.

Question 2

Does transcript text come through, and in what shape?

Accepted Answer

Yes, as an array of utterances nested inside each conversation record. Every utterance carries the transcript text, the speaker's ID, name, and type, the participant, its timing and length, word count, and engagement flags, and a separate speaker clusters array gives you who spoke across which ranges. That is richer speaker labelling than some conversation tools provide. The trade off is shape: the whole transcript sits inside one conversation row rather than as normalized sentence rows, so you unnest it in your data warehouse to query it line by line.

Question 3

How do incremental reads work?

Accepted Answer

Engagements and conversations sync incrementally, with deliberate overlap on both. Engagements filter from a cursor rounded back to the previous day boundary plus a twelve hour safety margin, and conversations, which are read as children of changed engagements, add a further two day overlap because a conversation keeps changing after the call ends as processing completes. Emails, scorecards, and users are full reads on each ETL execution. That overlap costs a little rereading and buys you not missing late arriving transcript content.

Question 4

Why are only some engagements available as conversations?

Accepted Answer

Conversations exist for meetings only, so an engagement that is not a meeting will not have one. Polytomic discovers meeting engagements first and then requests each conversation individually, which is why that object is the most request heavy in the connector. If your reporting only needs call metadata, participants, and tracker matches, engagements alone cover it and sync far more cheaply. Add conversations when you actually need the transcript content.

Question 5

Why move Chorus data into a data warehouse?

Accepted Answer

Because conversation intelligence tools can tell you what was said but not what it was worth. Whether calls mentioning a competitor close at a lower rate, whether a particular objection predicts churn, and how talk ratio correlates with deal size all need transcripts joined to CRM outcomes. Running ETL into your data warehouse makes those joins possible on your own definitions, and reverse ETL on the same platform can push a derived signal back onto the opportunity record reps work in.

Chorus

Perfect pairings

Transcripts with speakers

Tracker matches included

Incremental with overlap

Example workflows

What you can sync

From Chorus

To your destinations

How it works

How to set up

Frequently asked questions

Why teams choose Polytomic

No engineering required

Flexible data modeling

Handles scale automatically

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