Splits


Audience	Marketers and analysts who want to compare the effectiveness of different campaign strategies using real audience data.
Prerequisites	A saved audience At least one sync or campaign ready to launch

Run controlled experiments directly in Customer Studio by splitting audiences into randomized groups to test and measure the true impact of your marketing campaigns.

Learning Objectives

After reading this article, you will be able to:

Understand when and why to use randomized audience splits.
Set up A/B or multivariate splits within a saved audience.
Sync each split group to separate treatments, campaigns, or destinations.
Measure campaign effectiveness using split charts and statistical analysis.
Access holdout group logs for detailed post-campaign reporting in your warehouse.

Audience Splits enables you to create randomized audience segments for accurate, controlled testing of campaign strategies. By splitting an audience into distinct groups, you can compare outcomes between different treatments, channels, or creatives, gaining clear insights into what's truly driving your campaign performance.

What you can measure

Incrementality: Did your campaign actually cause the desired outcome?
A/B/n testing: Compare different creatives, messages, or channels.
Personalization impact: See if personalized experiences outperform generic ones.
Channel effectiveness: Identify which channels drive more downstream conversions.

When to use splits

Use audience splits when you want to move beyond assumptions like:

Correlation: “Purchases increased after my campaign—but did the campaign cause it?”
Attribution: “A user converted after seeing my email—but would they have converted anyway?”

Running a split test helps you isolate the effect of your campaign by comparing outcomes between randomized groups. This gives you a clearer view of what’s actually driving results.

How audience experiments work

Stage	What it means	Hightouch feature
1. Define your audience	Choose your target group	Build audiences
2. Split into groups	Randomly assign audience members	Splits
3. Apply a treatment	Run a campaign for one group	Syncs
4. Measure results	Compare outcomes between groups	Charts (Splits tab)
5. Interpret the impact	Evaluate statistical significance and lift	Splits Measurement

Example use cases

Email campaign holdout

Goal: Test whether sending an email increases purchases.

Split audience 50/50
Send promo to Group A, hold out Group B
Measure conversions or revenue per user

Ad creative A/B test

Goal: See which ad performs better.

Split audience into two groups
Sync to separate ad sets in Meta or Google
Compare CTR or conversion rates

Personalization test

Goal: Measure the impact of tailored content.

Group A: Personalized product recs
Group B: Generic message
Measure engagement, clicks, or sales

Lifecycle messaging experiment

Goal: Improve onboarding outcomes.

Split new users at signup
Sync to two onboarding flows
Measure feature activation or retention after 14 days

Channel comparison

Goal: See which marketing channel performs best.

Sync Group A to email, Group B to paid ads
Measure downstream conversion or LTV

Setup steps

1. Split your audience

Open your audience in Customer Studio.
Go to the Splits tab.
Toggle Enable split groups.
Under Split audience into groups, define your split:
- Set the number of groups (e.g., 2 for A/B tests, 3+ for A/B/n tests).
- Adjust the percentage distribution between groups (e.g., 50/50).
- Rename groups as needed (e.g., "Holdout", "Treatment A").
Optionally add more groups for A/B/n testing.
For each group, configure syncs to destinations.
- For example: send the treatment group to Meta Ads and the holdout group to a suppression list.
Click View Results to begin analysis.

2. Measure results

Campaign Intelligence charts and metrics are included by default with Customer Studio.

Clicking View results will open a Splits chart type.

Configure the splits chart →

Holdout group logs

Marketers typically rely on splits measurement charts to view campaign impact in the Customer Studio UI. Holdout group logs serve a different purpose for data teams: they provide row-level visibility for advanced analysis in SQL or BI tools.

Holdout group logs track which rows were excluded from a sync as part of a split group (e.g., a 20% holdout). These logs help data teams run detailed post-campaign analysis in the warehouse, such as identifying exactly who received a treatment and who didn’t.

How to enable holdout group logs

to enable the Holdout group logs feature flag. This requires:
- The Lightning sync engine
- A destination with write access to your data warehouse
Toggle the setting in workspace sync log settings
- Go to Integrations → Sources and select a source
- Navigate to the Sync Logs tab
- Check the box for Audience holdout group logs

When enabled, Hightouch writes excluded rows to your warehouse in the hightouch_audit.audience_holdout table. Each record includes the split_group, row_id, timestamp, and a snapshot of the source data. Rows that were synced are stored separately in the sync_changelog table—both must be joined for complete analysis.

Table definition

The hightouch_audit.audience_holdout table is an append-only log. If the same row is included in multiple sync runs, it has multiple entries in this table.

COLUMN	DESCRIPTION
`sync_id`	The ID of the sync
`sync_run_id`	The ID of the sync run
`model_id`	The ID of the model attached to the sync. This is the same as the audience ID.
`timestamp`	The timestamp the sync occured.
`row_id`	The value of the row's primary key as defined from the model
`fields`	A JSON object of the raw data from the model that is synced into the destination. Note that this is the raw data from the warehouse, not the payload that Hightouch sent to the destination. This column has limitations in Redshift (see FAQ for details, below).
`split_group`	The split group name.