How to measure campaign success
The metrics, thresholds, and testing rules to apply to every campaign.
Table of Contents
Every campaign should ship with three things: an experiment design, a primary metric, and a brand anchor. This isn't optional — don't launch a campaign without them. This article walks through the measurement framework, and how to design tests.
What the campaign builder can help create today: in-product experiences (Flows, Pins, Embeds, Checklists, Launchpads, Banners). Email, push, and Workflows aren’t part of the initial campaign product or Appcues AI building capabilities yet — where this article mentions them, treat them as a manual, separate setup, not something the AI can build for you.
Metric selection, in order
Follow this order:
- Start with your own metric, if you have one. If you have a defined success metric for the campaign, use it.
- If you don't have one, use a default from the stage-appropriate table below, with a 5–10 percentage point tolerance — if you're within that range of the benchmark, the campaign is working and you iterate from there.
- Always pair completion with a downstream-action Key Moment. Never report completion rate alone.
Why completion rate isn't a success metric
A Flow can have an 80% completion rate and drive only three signups against a target of hundreds. Completion tells you whether users clicked through — it doesn't tell you whether they took the action that matters.
Always pair completion with a Goal that tracks the actual downstream behavior. On any A/B test, make sure both variants track the same Goal so you're comparing outcomes, not just engagement.
Default metrics by stage
| Stage | Leading indicators | Working threshold |
|---|---|---|
| Awareness | Open rates, clicks, page visits | 15% exposure-to-completion floor for clickable experiences |
| Activation | Activation rate (your definition) | 15% completion of the targeted action; iterate below |
| Adoption | Daily/weekly usage of the activated feature | 30%+ retained at week 2 |
| Re-engagement | Return rate from email or in-product nudge | 10%+ return within 7 days |
| Expansion | Handraise rate or feature-trial rate | 5%+ of targeted users |
The adoption, re-engagement, and expansion thresholds are starting points, not industry benchmarks — adjust based on your product's specifics and your historical data.
Each stage also ladders up to a business outcome — and that outcome usually lives outside Appcues:
| Stage | Business outcome to pair with | Where it's usually measured |
|---|---|---|
| Activation | Time to Value, Activation Rate | Product analytics |
| Adoption | Feature Adoption Rate | Product analytics |
| Retention | Churn Rate, Retention Rate | CRM or billing |
| Expansion | Expansion revenue by segment | CRM or billing |
Appcues measures exposure, completion, and Goal conversion. It doesn't directly track revenue, churn, or account health — those live in your CRM, billing system, or product analytics. Pair your Appcues-side metrics with that outcome data to show the full impact, and don't assume a metric is provable in Appcues alone just because a campaign influences it.
The 15% floor
Use 15% exposure-to-completion as the floor for "is this experience working at all." Below 15%, iterate before anything else. Above 15%, look at the downstream Key Moment to determine actual success. The benchmark for completion itself is roughly 32% — use this as a reference point when your own data is thin.
When to run a test
Don't default to A/B testing everything. Run a test when:
- The audience is large enough. Any sub-segment in the test needs at least 150 users for the results to be readable. Below that, pick your best option, ship it, and learn from production data.
- There's genuine ambiguity. If you have a high-confidence choice, ship it instead of burning a test slot on something obvious. Tests are for real unknowns — different copy approaches, different experience types, different levels of interruption.
- You can measure it. Don't ship a test you can't evaluate rigorously.
How to design tests
- Variant construction: Duplicate-and-edit the existing experience, tied to the same campaign as a different variant. Don't build tests as separate campaigns.
- Key Moment or Goal alignment: Every variant tracks the same Key Moment or Goal. Outcomes, not just engagement, drive the decision.
- Traffic split: 50/50 for two variants, 33/33/33 for three.
- Duration: 3-week ceiling as a default. Check at day 3–5 — if the data is conclusive early, iterate; if it's still noisy, let it run.
- Alert: Set up a check-in window a few days into the test so you can review direction.
Example: Modal vs. no Modal
A common test to consider for an onboarding campaign:
| Variable | Setting |
|---|---|
| Control | 4-step Flow starting with a Modal, then 3 Tooltips. Modal asks the user to enter the tour via CTA. |
| Treatment | Remove the Modal, jump straight into Tooltips. Auto-decide for the user. |
| Traffic split | 50/50 |
| Goal | Same Goal on both — has the user completed the target action within 3 days of seeing either Flow |
| Duration | 3 weeks max; alert at day 3–5 |
The question this answers: does giving users a choice to enter the tour improve or hurt the downstream action?
Measurement mistakes to avoid
- No primary metric defined. The most common failure. Don't let a campaign ship without one.
- Vanity metrics treated as success. Exposure rate alone is vanity — it needs to be paired with completion and a downstream outcome.
- Measuring at the wrong stage. Adoption metrics on an awareness experience, or awareness metrics on an expansion campaign.
- Static list segments in tests. CSV-based segments age immediately — use dynamic behavioral segments for test targeting instead.