SERP Split
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Free SEO Split Testing • Test vs Control

Free SEO Testing Tool

Prove SEO impact without guessing.

SERP Split helps you create balanced test/control groups and analyse results with a clear uplift readout, so you can confidently roll changes out sitewide.

Create groups Read FAQs
Create groupsUpload URL + clicks CSV
Run testApply change to test group
AnalyseGet significance result
How it works

A simple workflow for SEO split testing

Inspired by the same principles as enterprise SEO testing platforms - just free.

Create test & control groups

Upload your URLs with historical clicks to generate clean test and control groups using stratified sampling.

Launch your changes

Apply a single SEO change to the test group while keeping the control group unchanged. Minimum of 21 days recommended.

Analyse impact on traffic

After the test period, return with data to measure uplift and confidence using Bootstrap Casual Inference.

The tool

Create groups & analyse results

Create your Test and Control Group

Upload a CSV containing columns: url, clicks. Then export your test/control lists.
Example columns: url,clicks (page-level clicks over ~100 days).
Upload daily data containing: date, test_traffic, control_traffic. Include pre-period (e.g. 100 days) + test period (e.g. 21–30 days).
Minimum recommended test length: ~21 days.
Results preview
Example output – your test results will appear here.
Uplift+X.X%
Absolute+XXXX clicks
p-value0.0XX
ResultSignificant / Not
FAQ

Common questions

SEO split testing measures the impact of a change by applying it to a test group of similar pages while keeping a comparable control group unchanged, then comparing performance over the same time period. Advanced methods like bootstrap causal inference also allow you to forecast expected performance and compare it to the actual results, giving a more accurate and statistically robust understanding of how the change influences SEO metrics.
This free SEO testing tool lets you run controlled experiments by splitting your pages into test and control groups using stratified sampling. Once your test ends, you upload daily performance data and the tool measures traffic uplift and statistical significance using a causal inference style analysis. The results will show actual uplift, relative uplift along with significance and a graph that presents the data.
You can test changes to titles, H1s, internal links, templated copy, AI content, FAQS, schema tweaks, performance fixes and anything you can reliably apply to only the test group. Keep it to one main variable per test for clear learning.
Typically 21–30 days. Longer tests reduce volatility effects (weekday/weekend swings, campaigns and seasonality).
More is better. As a rule of thumb, aiming for high total clicks across the pages in the last ~100 days improves statistical reliability and your ability to detect smaller uplifts.
Ideally you will validate the result if positive by analysing google search console data for the test period vs previous period, searching for the positional uplifts that impacted clicks. To validate position there is not a straightforward process, it requires cutting and slicing the query, page level data in different ways to find the positional uplift. It depends also on what the test is.
Not inherently, because you’re not cloaking or redirecting users and testing on a subset of pages. You’re applying real changes to real pages. The key is to avoid breaking critical elements and to keep a reliable control group.