A/B Split Testing Resources and Blog Posts to Help With SEO

Ever since I have heard about Distilled’s SEO Split Testing Platform, I have been obsessed with the idea to replicate this tool.

I have read every post I could find about A/B testing engineering, and made this list of resources I found really helpful.

Here are a few things that I learned.

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  1. It is hard to collect data properly for SEO Split Testing;
  2. The CausalImpact Package is far from perfect. It is hard to detect variations with the package. I will probably migrate to Neural Network instead;
  3. If you have to choose between learning R or Python, choose whichever you want, but after a while, I ended up using Python.

You might want to check out Trevor Fox’s post giving practical tips on SEO split testing.

First, let’s look at the original CausalImpact Package that inspired Distilled’s SEO tool.

SEO Split Testing Resources

Here are a lot of useful articles to learn to make your own SEO Split Testing.

Causal Impact

What is SEO Split Testing – Distilled

19 Lessons I learned from a year of SEO split testing

CausalImpact Package Documentation

CausalImpact Essay

Finding the ROI of Title tag changes using Google’s CausalImpact R package

Next Era of SEO: A Guide to SEO Split-Testing

Compare Actual Vs Predicted Data With Google Analytics And CausalImpact – Bounteous

CausalImpact Wrapper for Python

Improve CausalImpact Package

If you want to adapt CI package to become more precise you’ll need to investigate the BSTS model.

Bsts Model

Fitting Bayesian structural time series with the bsts R package – Steven L. Scott

Other Businesses Experiment Platforms

It’s All A/Bout Testing: The Netflix Experimentation Platform

Under the Hood of Uber’s Experimentation Platform

AirBNB – Experimentation & Measurement for Search Engine Optimization

Optimizing Meta Descriptions, H1s and Title Tags: Lessons from Multivariate SEO Testing at Etsy

Etsy – Double-bucketing in A/B Testing

Etsy – SEO Title Tag Optimization at Etsy: Experimental Design and Causal Inference

Pinterest – Demystifying SEO with experiments

Pinterest – Building Pinterest’s A/B testing platform

Pinterest – Scalable A/B experiments at Pinterest

Thumbtack – SEO Tip: Titles matter, probably more than you think

Thumbtack – A/B testing at Thumbtack

Indeed – Proctor A/B Testing Framework

LinkedIn – From Infrastructure to Culture: A/B Testing Challenges in Large Scale Social Networks

Lyft – Part 1 of 3: Experimentation in a Ridesharing Marketplace

Lyft – Part 2 of 3: Simulating a ridesharing marketplace

Lyft – Part 3 of 3: Bias and Variance

Twitter – Implications of use of multiple controls in an A/B test

DIY Split Testing

Extract Data From Google Trends Using Python – DataCamp

SEO: Get started 10x’ing your traffic

A/B Testing With Google Tag Manager

A/B-testing via GTM – free with unlimited traffic

More JavaScript SEO experiments with Google Tag Manager

How to Implement SEO Changes Using Google Tag Manager

Ideas Of SEO Tests That You Can Run

SEO split tests you should run – Will Critchlow

Rand Fishkin’s 5 Simple Experiments for Improving SEO Health

A Journey Through SEO Testing with ODN (Videos)

SEO Split Testing Tools


Distilled ODN


Google Optimize


Research Papers

Orthogonal Random Forest for Causal Inference

Causal Inference and Stable Learning

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