Why SEO Automation?

People often ask us: what is SEO automation and why do we need it?

In this post, we’d like to share our thoughts and our answer in the following aspects that compare SEO with search engines:


The Volume – Search Is Big Data

According to SmartInsights, there are now over 6.5 billion searches made worldwide every day, specifically, this number is:

6,586,013,574 SEARCHES IN A DAY!”

This number is quite staggering and just imagine that on average people on earth sent 76,227 searches on every second to search engines.

So how many unique search queries are out there? We cannot find exact answers from search, duh.

But we looked into our data. As an empirical data point, people from Bellevue, Wash. USA searched 2,480 times on plastic surgery and related services on August 30, 2018. They used 699 different search queries from Google alone in English.

plastic urgery Bellevue WA

We observed a human analyst’ difficulty of using manual methods with some 3rd-party tools to try to collect, analyze and make sense out of all the searches, just for this single business, DAILY.

SEO automation will help collect, process, and analyze search big data, even for main-street local business.


The Smartness – Search Ranking Is Going AI

By now you should have heard of Google’s not-so-new search ranking AI model called RankBrain. According to what we know, RankBrain is machine-learning AI system that used by Google starting in late 2015. At the beginning, RankBrain was used for less than 15% of queries, but now it impacts all search queries Google processes. Further more, in a 2015 interview, Google commented that RankBrain was the third most important factor in the ranking algorithm along with links and content.

Based on our observation of advancement of AI and machine learning on big data, we predict that Google and other search engines are going to be more and more reliant upon AI models to rank search results more relevant to you and me who search.

What does this mean to the SEO industry? The known SEO rules as we know and are using today could be invalidated by search engines AI models. Even when these rules persist, which of them are active and their weights can be changed quickly or different from query to query – all from what they learn from the big data.

In response, the SEO industry needs to develop a responsive solution using the same AI and machine learning technology that learns from engines’ ranking results and from which derives SEO factors that are customized to each business category, each business and even each search query, at the time of the day.

This is another aspect on why we need SEO automation.


The Measure – SEO Needs a Business Metric

Traditionally, the goal of SEO has been to improve your search ranking. Sounds all good? Not yet – don’t forget that search ranking does not equal to customers into the door. Rather, it is a technical measure invented and used by SEO technologist to brag about their success. Businesses need a higher-level overall measure that tells how much of the search market share you are having today.

For example, SearchDom  straightforwardly uses the Search Dominance Score (or D Score for short) that is the percentage of your search market.

Now let’s go back to the plastic surgery search market in Bellevue, Wash., USA. we used in this post. A plastic surgery business in this town currently has a D Score of 0.11. This means that they receive 11% of all search volume on a day.

D Score

Now we have a number that businesses understand and SEO can be used to improve.

Replacing keyword-specific page ranking with market-overall D score requires data aggregation and calls automation.


The Speed – SEO Demands More and Faster Action

Every SEO practitioner knows this: since the inception of SEO about 25 years ago, it has stayed as a labor-intensive human process. Even with all the tools available, human is still needed in SEO. Labor will be spent in search data collection, analysis, and optimization action. Today you can find tools to help in the collection and data reporting. But human brains are still needed in looking thru the data and reports, analyze and figure out what actions to take to lift a business’ presence on search.

There are 200+ major search ranking factors Google uses (and many many more “sub” factors). How many actions can you take in a day to optimize them? Well, each of us only has 24 hours a day at max.

Don’t you wish to have automation to check all factors daily, find needed optimization work items and execute for you?

The Consistency – SEO Must Have a Response Loop

Due to human limit, most of SEO guys do not or cannot establish and execute an SEO response loop, where you consistently examine results of your past action, learn from resulted search presence changes and revise your action as needed, then look at all data points again to come with the next batch of action items. Better yet, can you, a human, do this daily or even in real time?

I am sure we have all heard of this comment: “SEO is slow and is not working.”

It is working, but needs automation to work.


Conclusion: SEO vs. Search Engines

Since 2015, the gap between human-intensive SEO and machine automated search ranking on big data has become wider, due to search engines’ adoption of AI ranking models.

From what we observe, human has lost this battle to conduct effective SEO against search engines AI ranking on all fronts possible:

  1. DISCOVERY. We can only analyze limited amount of data and as the result draw limited conclusions on where we are in search and how to optimize;
  2. ACTION. We cannot execute optimization actions fast enough to get results needed in time;
  3. LOOP. We cannot map resulted search market changes at all or fast enough, learn from past actions and use what we learned to improve our next actions;
  4. NO RULES. Search engines may invalidate all SEO rules we know today.  AI learns from data and does not use rules. Even if rules stay, AI has the ability to customize rules down to each search query.
  5. BUSINESS IMPACT. SEO industry needs to aggregate the technical measures such as rankings into a single business measure such as the D Score that shows how the SEO work is impacting businesses’ bottom lines.

seo vs search


The SEO Automation Time Has Come

It is clear to us by now that the SEO industry needs to develop end-to-end automation process from data to action to the feedback loop, leveraging AI to machine learn from the search big data, IN REAL TIME. Time to relieve our practitioners from the low-effective and high-labor tasks of: data collection, keyword discovery, search market identification, search market competitive analysis, competitive analyzes of search results, identification of high-impact optimization work items, automate the action execution, and create and use a real-time action-to-result feedback loop to continue improving.

Therefore, the end result: the smart SEO that continues to improve the business bottom line non-stop.

This is our mission.


Some recommended background reading:

About the author: Lee Wang

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