Launch HN: RankScience (YC W17) – Automated Split-Testing for SEO

RankScience automates split-testing for SEO to grow organic search traffic for businesses. 80% of clicks from Google go to organic results and yet most companies don't know how to improve their SEO, or can't effectively measure their efforts to do so. Because of the scale of SEO and the constant change of both Google's ranking algorithm and your competitors' SEO campaigns, the only way to succeed in the long-run is with software and continuous testing.

We've built a CDN that enables our software to provide tactical SEO execution and run A/B testing experiments for SEO across millions of pages. Experiments typically take 14-21 days for Google to index and react to changes, and we use Bayesian Structural Time Series and Negative Binomial Regression models to determine the statistical significance of our experiments.

Our software is 100% technical SEO, and doesn't do anything black-hat, spammy, or anything related to link-building. One of our goals is to bring transparency and shed light on what is largely considered a shady industry, but is so important to so many companies' revenue and growth. In fact, If SEO didn't have such a bad reputation, we think someone else would have built this a long time ago.

SEO as an industry earned itself a stigma for being spammy: between buying links, creating low-quality pages stuffed with keywords and text intended for Google rather than humans, and the used car salesmen attitude that many SEOs have, many people have been conditioned to dismiss SEO as an invalid or illegitimate growth channel.

We're software engineers-turned-SEO's, who have previously consulted for dozens of companies on SEO, from YC startups to Fortune 500 companies like Pfizer. We previously shared our case study with HN, where we increased search traffic to Coderwall with one A/B test: https://www.rankscience.com/coderwall-seo-split-test

Ask us anything! We'd love to answer any questions you have about SEO, A/B testing, and RankScience.

Looks great, congrats on the launch!

Two points of feedback:

(1) The idea of the product is clearly conveyed, but I'm confused on exactly how it works. The landing page mentions that title tags, headlines, meta-tags, etc get tweaked - exactly how is this done? Do I have to manually enter a bunch of alternative text, or are you using a big fancy thesaurus to switch out some key terms?

(2) How do you evaluate performance of the product? Solely through click rates, or by search rankings? How often do google search results get updated? In short, how do I know the product is working?

Thanks sgslo!

(1) We use both humans and software to generate experiments. For customers, it's completely automated.

(2) We look at all primary search metrics (clicks, impressions, CTR, and rankings), with clicks being our main metric. Search results get updated at a pace determined by Google's crawl rate, which varies per site depending on multiple factors including domain authority. We use bayesian structural time series and negative binomial regression models to measure impact and statistical significance to power our data-driven SEO recommendations.

Though using A/B testing to improve user experience or conversion rates is fine, I thought using A/B testing to reverse engineer the ranking algorithm was against the guidelines. Has this been updated?

From https://support.google.com/webmasters/answer/7238431?hl=en

> Best practices for website testing with Google Search

> The amount of time required for a reliable test will vary depending on factors like your conversion rates, and how much traffic your website gets; a good testing tool should tell you when you’ve gathered enough data to draw a reliable conclusion. Once you’ve concluded the test, you should update your site with the desired content variation(s) and remove all elements of the test as soon as possible, such as alternate URLs or testing scripts and markup. If we discover a site running an experiment for an unnecessarily long time, we may interpret this as an attempt to deceive search engines and take action accordingly. This is especially true if you’re serving one content variant to a large percentage of your users.

Next to this, the advice is to use rel="canonical" to avoid duplicate issues with Googlebot crawling your variations. When using rel="canonical" this should not show you how a variation influences ranking.

> If you’re running an A/B test with multiple URLs, you can use the rel=“canonical” link attribute on all of your alternate URLs to indicate that the original URL is the preferred version. We recommend using rel=“canonical” rather than a noindex meta tag because it more closely matches your intent in this situation.

My post was in response to the homepage copy:

> RankScience sits next to your website, making thousands of experiments to tweak your HTML in order to improve your page rankings.

This seems to me an attempt at reverse engineering the ranking algorithm. Is my interpretation correct? And if so, is this allowed / in scope of the guidelines?