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

    RankScience

    Distilled ODN

    CRO SEO Tools

    Google Optimize

    VWO

    Research Papers

    Orthogonal Random Forest for Causal Inference

    Causal Inference and Stable Learning

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