Holdout Design for Triggered Emails and Push Notifications (2025 Best Practices for eCommerce & SaaS)

1 September 2025 by

WarpDriven

If you automate lifecycle messaging but can’t prove incremental lift, you’re flying blind. Holdouts are how you move from correlation to causation. In practice, that means randomly excluding a slice of otherwise-eligible users from a triggered journey and comparing their outcomes to those who received the message. Done right, you’ll know whether your cart reminders, onboarding nudges, or reactivation pings truly drive revenue—or just harvest what would have happened anyway.

According to the 2024 Omnisend ecommerce study, automated emails (triggered flows) generate 41% of email orders while accounting for only 2% of sends—underscoring their potential when measured and optimized for incrementality Omnisend 2023 Email, SMS & Push Report (published 2024). And randomized holdout tests are the accepted way to measure that true impact, as shown in platform documentation and experimentation literature from Customer.io — Holdout test, Airship — Holdout experiments, and Measured — Holdout test FAQ.

What follows is a practitioner’s playbook: the exact steps, guardrails, and trade-offs to design credible holdouts for triggered email and push in 2025.

1) What a Holdout Is—and When It’s Worth It

A holdout (control) is a randomized subset of eligible users who are intentionally withheld from a triggered message or journey. By comparing conversion and revenue between exposed and holdout groups, you estimate incremental lift.

Why not just use pre/post or a similar audience as a “control”? Because selection bias, seasonality, and underlying engagement differences will mislead you. Randomization equalizes both observed and unobserved factors, enabling causal inference—this is emphasized in 2025-era docs by Customer.io on holdouts and the incrementality fundamentals outlined by Measured’s holdout FAQ.

When to prioritize holdouts:

High-volume, high-revenue flows (cart/browse abandonment, onboarding/activation, churn prevention)
Moments where leadership questions attribution (“Would they have bought anyway?”)
Before rolling out major changes (new sequence, new channel, frequency shifts)

Trade-offs:

Opportunity cost: holdouts forgo revenue while testing
Operational complexity: orchestration, suppressions, and analysis discipline
Patience required: enough time and sample to detect meaningful effects

2) Core Design Decisions: Sample, Split, Duration, and KPIs

Start with the decision checklist:

Randomization point: Prefer flow entry for journey-level lift; message-level holdouts for smaller questions
Holdout proportion: 10–20% is a pragmatic starting range for most programs (balance power vs. opportunity cost), aligned with incrementality practice in 2024–2025 guidance like Singular’s incrementality overview
Duration: Plan for 2–4 weeks minimum (longer for low volume) and fix the horizon to avoid “peeking” bias, consistent with experimentation best practices summarized by CXL’s A/B testing guide
Primary KPI: Pick one primary business outcome (e.g., incremental order rate, incremental revenue per recipient) and pre-register it
Minimum Detectable Effect (MDE) and power: Size your sample to detect a realistic lift with 80–90% power at 5% alpha; helpful primers and calculators are covered in Statsig’s MDE expectations

Definitions you’ll actually use:

Incremental conversion (pp): treatment conversion − holdout conversion
Incremental revenue per recipient (iRPR): treatment RPR − holdout RPR
Total incremental revenue: iRPR × eligible population over test window

Guardrails:

Don’t change allocation mid-test
Use a fixed stopping rule, or if you must monitor continuously, apply sequential corrections (e.g., alpha spending) as described in CXL’s peeking overview

3) Implementation Patterns by Tool (What Actually Works)

You can run credible holdouts on most leading platforms. A few patterns and caveats matter in 2025:

Customer.io (Journeys): Use the built-in Holdout Test or Random Cohort Branch; route the holdout path to an internal blackhole so no message is sent, enabling clean lift analysis. See Customer.io holdout docs and cohort testing.
Braze (Canvas): Create an Experiment Path with a “no send” holdout branch (or a null message path). Winning Path can automate allocation after significance, but keep it disabled during the initial lift test; details in Braze’s Canvas experiment docs.
Klaviyo: Use Global Holdout Groups to exclude a percentage of profiles program-wide (requires large audiences and typically ~3 months). Be aware that flows can override global holdouts, so document any exceptions. Reference Klaviyo global holdout guide.
Iterable: Holdouts are configured via Campaign Experiments for “blast” sends. Current docs do not expose true holdout controls inside triggered journey steps; plan holdouts at the campaign level and simulate journey comparisons carefully. See Iterable – Configuring Experiments.
Airship: Experimentation supports holdout experiments across channels and goals, including global controls. Useful for unified messaging programs. See Airship – Holdout experiments and product experimentation overview.
OneSignal: While not branded as “holdout,” you can create control cells by excluding a random percentage or using Journeys with a no-send branch; consult experimentation and Journeys resources like OneSignal’s practical tips.

Pro tip: Whatever the tool, validate randomization integrity early—check for sample ratio mismatch (SRM). A simple chi-squared check will flag if your 80/20 split is coming out 72/28 due to filters or delivery constraints; see Statsig’s SRM overview.

4) Control Contamination and Channel Spillover

Holdout design breaks when users get parallel nudges from other channels. Tactics that contain spillover:

Apply suppression lists and channel eligibility rules so holdout users don’t receive alternate nudges (e.g., SMS/push) during the test window; see Braze suppression lists
If you’re testing email impact, consider suppressing push for the same users until test end; keep frequency caps stable and consistent across groups, aligned with Braze push reporting/frequency guidance
When spillover risk is high (shared devices, multi-channel orchestration), design geo test cells instead of user-level splits. Measured argues that representative geo selection outperforms synthetic controls alone in many real programs; see Measured on geo testing and synthetic controls
If you need to equalize the “notification surface” effect, use inert/PSA messages for holdouts so both groups see a comparable interruption, while avoiding promotional content

5) Analysis Workflow: From Integrity Checks to Variance Reduction

A streamlined analysis approach I’ve found reliable:

Integrity checks first
- Confirm event logging is complete and consistent
- Run SRM check on allocations (e.g., chi-squared). If SRM is present, stop and fix randomization before interpreting results, per Statsig’s SRM guidance
Compute incremental lift and intervals
- Primary metric: incremental conversion (pp) and iRPR
- Use appropriate tests for proportions/means; pre-specify two-tailed alpha = 0.05
Reduce variance where appropriate
- Apply CUPED with pre-period behavior (e.g., prior week’s purchases or engagement) to tighten confidence intervals without biasing the estimate, as detailed by Microsoft Research on CUPED
Guard against multiplicity
- If you tested multiple variants or KPIs, control family-wise error or FDR using standard procedures (Bonferroni or Benjamini–Hochberg), a core tenet in modern experimentation primers like CXL’s A/B testing guide
Interpret and decide
- If lift is significant and positive, ship and set up a smaller “sentinel” holdout for continuous monitoring (e.g., 5%)
- If inconclusive, extend duration or increase sample; for low-volume flows, pool across similar triggers or apply CUPED and stricter targeting
- If negative, diagnose contamination, creative, timing, and deliverability before discarding the flow

6) Use-Case Playbooks (eCommerce and SaaS)

These are the flows where holdouts pay for themselves fastest.

A) eCommerce

Cart Abandonment

Setup: Randomize at flow entry; 10–20% holdout; suppress SMS/push for holdout until test ends
KPI: incremental checkout or purchase rate; iRPR
Duration: 2–4 weeks minimum
Notes: Seasonality matters; consider running through at least one promo cycle. Omnisend’s 2024 data shows triggered automations materially outperform campaigns on conversion, implying strong lift potential when set up properly Omnisend 2024 automation performance

Browse Abandonment / Price Drop

Setup: Message-level holdouts on the first nudge; use CUPED with pre-browse engagement covariates
KPI: incremental product view-to-cart and purchase; attach AOV change cautiously
Watch-outs: On-site promos can confound results; document concurrent offers

Post-Purchase Cross-Sell

Setup: Holdout at message level; maintain consistent frequency caps across groups
KPI: incremental attach rate and AOV; monitor returns/cancellations to ensure no downstream harm
Notes: Consider a PSA control if you suspect “attention” effects from any notification

B) SaaS

Onboarding/Activation (new users)

Setup: Randomize at entry to the onboarding journey; 10–20% holdout
KPI: activation events (e.g., key feature used), trial-to-paid conversion, Day 7/30 retention
Notes: Suppress in-app prompts for holdouts if testing email/push impact specifically; otherwise isolate channels with Intelligent Channel off

Trial Conversion (mid-journey nudges)

Setup: Message-level holdouts on key milestone nudges
KPI: incremental trial-to-paid conversion, time-to-convert
Notes: For enterprise trials (low volume), keep tests running longer or pool cohorts by plan tier

Churn Prevention / Dunning

Setup: Time-bound holdouts covering full billing cycles; ensure billing retries and in-app notices are harmonized
KPI: incremental recovery/reactivation rate; retained MRR
Notes: Avoid overlapping with customer support escalations which can contaminate both groups

7) 2025 Realities: Deliverability, OS Changes, and AI

Email deliverability rules got stricter in 2024/2025: Gmail and Yahoo require authenticated sending (SPF, DKIM, DMARC), low spam complaint rates, and one‑click unsubscribe—factors that directly affect observed engagement and your test read. See Braze’s 2024 deliverability update summary and related guidance.
iOS push changes and APNs updates: Keep SDKs and trust stores current; Apple announced APNs server certificate updates with 2025 timelines. Delivery breaks can masquerade as negative lift. Track Apple’s official updates via Apple Developer news index.
AI/adaptive experimentation: Multi‑armed bandits and Intelligent Channel/Timing can accelerate optimization but complicate pure lift measurement if allocations change mid-test. Establish efficacy with a clean randomized holdout first; then layer optimization. See Statsig on bandits vs A/B and Braze Intelligent Channel.

8) Scaling and Maintenance

Move from “proof” to “monitoring”: After a successful lift test, reduce to a 5% sentinel holdout to watch for drift. Re-run full tests quarterly or after major creative/platform changes
Governance: Maintain a shared registry of active holdouts, KPIs, start/end dates, and exclusions. Prevent overlapping tests on the same audience
Small programs: If volume is thin, pool similar triggers, extend duration, use CUPED, and set realistic MDEs
Mature programs: Rotate geo cells for brand-wide promos; set channel‑level caps and use orchestrators carefully to avoid contamination

9) Quick Checklists

Pre‑launch

Define primary KPI, MDE, power, and duration; document stopping rule
Pick randomization point (flow entry vs message level) and holdout % (start 10–20%)
Configure suppressions and caps to avoid cross‑channel contamination
Validate tracking, attribution windows, and deliverability/permissions

During test

Monitor allocation for SRM; pause if imbalance persists
Watch deliverability/permission health (SPF/DKIM/DMARC, APNs errors)
Don’t change splits or creative mid‑test unless pre‑planned with sequential methods

Post‑test

Compute incremental conversion and iRPR with confidence intervals; apply CUPED if pre‑period data exists
Decide: ship, extend, or rollback; set a sentinel holdout for monitoring
Archive results; schedule a retest cadence (e.g., quarterly)

Common Pitfalls (and Fixes)

Peeking and early stopping: Use fixed horizons or sequential corrections, per CXL’s peeking guidance
SRM from filters/deliverability: Audit entry conditions and channel eligibility; apply SRM checks as in Statsig’s SRM primer
Mixed exposures across channels: Enforce suppressions; consider geo cells per Measured’s geo testing notes
Over‑indexing on opens: Opens are noisy; optimize for conversion and revenue. Use tap rate for push; watch opt‑outs and complaints
Underpowered tests: Increase duration or holdout %, pool flows, or apply variance reduction per Microsoft’s CUPED write‑up

Final Word

Holdouts are not a luxury—they’re how lifecycle teams prove and improve impact. Start with a simple 80/20 split at flow entry, keep your orchestration clean, and read results with statistical discipline. Once you’ve established lift, shrink to a sentinel control and let AI‑assisted optimization take the wheel—without losing the ability to tell what’s truly moving the needle.