How to experiment, find winners, and personalize without the dev queue

Sid Chaudhary

Sid Chaudhary

Founder & CEO

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Deliver web and mobile experiences that learn fast and feel personal.

About the Growth Play

Conversion lifts don't come from more banners—they come from clearer tests and timely follow-ups. This Growth Play shows how to use GrowthOS to run Experiments on web or mobile, pick winners with a stats engine you can trust, turn those winners into targeted personalizations, and add a light activation layer with Journeys. The aim is a steady loop: try → learn → apply → follow up → measure.

TL;DR – GrowthOS for CX Marketers

Create an Experience for Web or Mobile and set a target page or screen.

Start an Experiment with multiple variants (layout, copy, offers, URLs).

Target by audience (returning users, campaign traffic, high-intent segments).

Trust results with Sequential Testing, CUPED variance reduction, and confidence intervals.

Apply the winning variant, then launch personalization for specific segments.

Add a Journey to re-engage drop-offs across email, push, or SMS with simple branching.

Track lift in Experience Analytics and campaign impact in Journey Analytics.

Benefits

Learn faster: Test ideas with a visual editor; ship small, see impact quickly.

Personalize with purpose: Target segments that actually differ.

Stay coordinated: Site/app and messaging work from the same audiences.

Reduce handoffs: Create, test, and apply changes without a long dev queue.

Prove lift: Use a stats engine designed to avoid false positives and noise.

How It Works

Step 1: Create Segments to personalize the experiences

Create segments for different users like those who are 'Ready for Upgrade' or 'Haven't logged in for more than 7 days'.

Step 1

Step 2: Create an Experience (Web or Mobile)

Choose the page or screen, open the visual editor, and outline the change you want to test (layout, copy, offer, or placement).

Add variants with their own display rules, targeting, and success goals. Target returning users, specific campaigns, or a high-intent segment.

Step 2

Step 3: Start an Experiment with clear targets

Add variants with their own display rules, targeting, and success goals — focused on the audiences you want to examine.

Step 3

Step 4: Let the Stats Engine do the math

Monitor results with Sequential Testing (safe peeking), speed them up with CUPED, and read confidence intervals to focus on meaningful lift.

Step 4

Step 5: Apply the winner and personalize by segment

End the test at significance, apply the winning variant, and create a personalization that shows the best version to the audience that benefits most.

Step 5

Step 6: Add the activation layer

If a variant reveals a drop-off, start a Journey for that audience:

Email first, then push or SMS, with simple delays and outcomes.

Add a Slack alert if a human touch helps.

Step 6

Step 7: Measure and iterate

Check Experience Analytics for lift and Journey Analytics for engagement and goal conversion. Keep what works; queue the next test.

Step 7

Good to Know

Define one primary goal per test; keep variants small so you learn what changed.

Use the same Segments for targeting and follow-up to stay consistent.

Keep copy blocks reusable with Smart Snippets so updates stay in sync.

Run fewer, clearer tests; ship winners quickly, then move on to the next question

Frequently asked questions. Answered.

Create an Experience, choose the page or screen you want to test, and open the visual editor. Make your changes directly (layout, copy, offer, placement) and add variants. Set targeting rules, define your success goal, and launch. The whole flow happens in the platform without writing code or submitting dev tickets.

Traditional A/B tests penalize you for peeking at results early. Sequential Testing is designed for safe monitoring. You can check progress as data comes in without compromising statistical validity. When confidence thresholds are met, you know it's real, not a false positive from checking too often.

CUPED (Controlled-experiment Using Pre-Experiment Data) uses historical user data to reduce variance in your test results. This means you reach statistical significance faster with smaller sample sizes. Use it when you have good historical data and want to speed up test cycles without sacrificing confidence.

Confidence intervals show the range where the true lift likely falls. If the interval doesn't cross zero and the range is tight, you have a meaningful result. If it's wide or crosses zero, you need more data or the effect isn't strong enough to act on. Focus on intervals, not just point estimates.

Start a Journey targeting users who dropped off at that point. Send an email first, then follow up with push or SMS if they don't engage. Add simple delays between steps and outcome-based branching. If it's a high-value segment, include a Slack alert so someone can reach out personally.

Smart Snippets let you create reusable content blocks that update everywhere they're used. If you change a headline or offer in the snippet, it updates across all experiments and personalizations referencing it. This keeps messaging consistent and saves you from manually updating the same copy in multiple places.

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