Question 1

What can I sync from BigQuery into Customer.io?

Accepted Answer

People and their attributes, custom objects, segments, person events and people devices. Between them that covers what a messaging tool needs to know: who somebody is, what they did, and which group they belong to. Devices are append only, so they are added rather than matched and updated. The rest follow normal matching, where you map a field from your BigQuery model to the identifier Customer.io uses.

Question 2

Do I have to create attributes in Customer.io first?

Accepted Answer

No, and this is a genuine convenience. New attributes and properties can be declared straight in the mapping for people, events and custom objects, so pushing a new field from your model does not need setting up on the Customer.io side beforehand. Adding something like a churn score or plan tier to your messaging becomes a change in one place rather than two.

Question 3

What if someone in my BigQuery model is not in Customer.io?

Accepted Answer

It depends on the mode. Update or create will create them, update only will skip them, and create only will create the new ones while leaving existing matches untouched. Pick based on whether Customer.io or your data warehouse should introduce new people. If a related record referenced in a mapping cannot be found, Customer.io can optionally ignore the bad relationship and still write the base record instead of failing it.

Question 4

How do I stop each sync re-reading the whole model?

Accepted Answer

Give it a tracking field. Model syncs are differential by default, but without a tracking field that still means running the query and comparing the output each run. With one, typically an updated at column that always advances when a row changes, Polytomic asks BigQuery for the maximum value first, then reads only rows past the last saved mark. On a large audience model that is a meaningful difference in both time and query spend.

Question 5

Should campaign segments be built in Customer.io or in BigQuery?

Accepted Answer

In BigQuery, for anything that depends on more than messaging behaviour. Usage trends, account health and churn risk are built from product, billing and CRM data together, and none of that is visible inside a messaging tool. Rebuilding the logic there means maintaining two versions that slowly disagree, which is how a campaign ends up targeting people your reporting says already left. Reverse ETL sends the model you already trust, so both sides use one definition.

Sync BigQuery to Customer.io

Sync BigQuery to Customer.io with Polytomic

People, events and objects

Declare attributes in the map

Bad rows do not stop a run

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Step-by-step integration guide

Connect BigQuery in Polytomic

Connect Customer.io in Polytomic

Create a sync between BigQuery and Customer.io

Map fields and enable the sync

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