With the advent of algorithmic updates like Google’s RankBrain, machine learning has become an integral component in how search engines understand and rank web pages.
As a result, SEOs and other marketers are in a position in which understanding machine learning is an increasingly vital perception to possess for optimizing client sites for search visibility.
With search engines like Google growing exponentially more sophisticated in identifying keyword stuffing, contextually inconsequential backlinks and thin content versus quality materials that will likely resolve user queries, marketers are continually turning toward machine learning solutions to meet the more advanced requirements of search engines.
While it is necessary for marketers to craft effective eCommerce SEO strategies that utilize contemporary white-hat tactics and best practices, employing machine learning technologies enables SEOs to achieve more prosperous results by providing more insightful opportunities for enhanced rankings at faster speeds.
Here, I aim to explore how machine learning systems are currently implemented within our organization and how these types of technologies are poised to impact the SEO industry over the next several years.
To begin, I will explore the foundation of all SEO efforts: Keyword research.
Using Machine Learning to Drive Keyword Research
Performing keyword research for SEO purposes is a critical component in developing a blueprint that will rank pages effectively. However, this process is often time-intensive and prone to human error.
To help remedy these pitfalls, Visiture has developed a proprietary, in-house keyword research tool utilizing machine learning programs to help weed out inefficiencies.
Created using several APIs and scripts, in conjunction with Google Sheets, Visiture has automated the keyword research process as this program receives an input of broad identifier keywords, which it then runs through the APIs to uncover phrase match and other keyword match types to generate an initial keyword list.
From there, the tool runs the website in question through several scripts to establish industry competitors who employ the same or similar phrases. As a means of competitive measurement for SEO, the system then runs the keyword lists of competing sites through the same scripts and APIs to identify any unique keywords that were not recorded in the initial listing for inclusion.
Finally, the tool then pulls currently high ranked keywords from Google search console (as it relates to the site in question) and runs them through the APIs and scripts to put together the final keyword list.
Once this process has been completed, the program pulls all the lists together, removes any duplicate terms and re-organizes them based on keyword phrases, thereby enabling Visiture to generate keyword lists with thousands of relevant words and phrases in a matter of minutes.
By employing such machine learning technologies to drive the keyword research process, our company has cut down what would manually be a 10 or more-hour project into an automated process that takes roughly 15 minutes to run.
Scaling this to a wide array of clients, this tool saves our team literally hundreds of hours in keyword research while simultaneously eliminating human miscalculations.
For instance, when going through lists of thousands of keywords over a significant period of time, it becomes extremely easy for a person to skip over keywords. Moreover, such a machine learning program also removes the potentiality of human bias, pulling relevant keywords based on the different research and algorithms versus what a human may “think” is applicable.
Finally, this system also aids in thwarting the potential dangers of keyword cannibalization issues that may arise from human oversight.
However, this is not the only way that Visiture is putting machine learning technologies to work.
Optimizing Pages Through Machine Learning
While obtaining keyword information through the processes outlined above has dramatically increased Visiture’s ability to gather the information required to elevate our client’s pages, it is another in-house tool that enables us to optimize those pages for better rankings.
Utilizing Python and Google Sheets, this proprietary tool goes to work by first harvesting considerable amounts of data pertaining to a site’s content library, title tags, meta descriptions and H1 tags and other technical elements. It then runs gathers the same information from the top 10 competing sites. At the same time, the tool pulls all the keywords that each page ranks for, along with its corresponding rank.
From here, we use a neural network-based algorithm to train the machine learning program based on all the information that has been pulled into the system; this process takes several hours to run its course.
From there, the machine learning algorithm then assigns keywords to each page via an excel spreadsheet and produces a projected ranking for a destination, assuming that it were to be optimized based on the information the system has learned about the existing content around the web and for what those pages were ranking.
Effectively, this means that SEOs can utilize automation and machine learning to automatize the process of understanding how to enhance a page’s SEO performance by assigning keywords to build content around and to optimize title tags, meta descriptions, category and product tags and other technical SEO elements.
However, this is just the beginning for Visiture as we see a variety of future opportunities to utilize automation and machine learning to enhance SEO outcomes.
The Future of Technical Auditing
Conducting technical SEO audits is a necessary component for optimizing and ensuring the overall health of a website as it relates to search rankings.
As it stands, there are a number of powerful SEO audit automation tools like Screaming Frog and Sitebulb, each of which provide considerable amounts of valuable data.
Using tools like these, SEOs and marketers can reveal optimization opportunities by crawling a site to uncover URLs blocked by robots.txt, meta robots or tag directives, reveal broken links, analyze the effectiveness of page titles and meta descriptions, in addition to a variety of other crucial technical audit tasks.
While the information that these tools have been able to provide SEOs has been invaluable to marketers across the industry, there is still room for innovation in the newly emerging world of machine learning.
Going forward, Visiture aims to produce automated, machine learning algorithms that are capable of taking the information provided by various SEO tools such as those mentioned above and other technical SEO tools and combining the information harvested into a single report.
By creating a series of rules and structures around connecting technical audit reports, Visiture seeks to establish reporting information that surpasses what current tools offer, by virtue of pulling from multiple tool sets into one readable report, as well as from in house proprietary tools.
Moreover, by automatically merging a variety of other data sources such as those from Google Search Console, Google Analytics and data from SEO tools into one report, Visiture will become capable of procuring a 360-degree view of a site’s current level of optimization, as well as the opportunities present to further enhance a site’s rankings.
Automating High-Quality Content Generation
Machine learning can have an incredible impact on a brand’s content marketing strategy, given that creating a cohesive content strategy is often reliant on obtaining and analyzing data that will allow a brand to optimize its site for specific keywords and topics.
For a variety of brands (such as our clients), employing automation tools to harvest valuable data is what helps to elevate a site in the SERPs. It is for this exact reason that many of today’s content marketing tools utilize automated algorithms to aid marketers in improving SEO performance.
For example, tools like HubSpot and BuzzSumo enable businesses to receive automated notifications if they have been mentioned across social platforms, instead of continually being forced to check for such references manually.
Alternatively, tools like Atomic Reach employ AI and machine learning technologies to craft more effective content strategies by focusing on critical keywords and phrases within content and reworking them to enhance performance.
Moving into the future, Visiture aims to one day develop tools to automatically generate dynamic content that is focused on high intent user keywords.
While actualizing such a goal is still a way off, it is something that we have our eye on as automation and machine learning technologies continue to evolve.
As predicted by Gartner, “More than 40 percent of data science tasks will be automated by 2020, resulting in increased productivity and broader usage of data and analytics by citizen data scientists.”
While this prediction may have been slightly off, automation and machine learning are certainly playing ever-increasing roles in business intelligence. The reason for this uptick is simple: intelligent programs and algorithms can analyze more data in a shorter amount of time than any person could possibly examine.
The fact is that, as exponentially more data continues to pour into marketing agencies on SEO performance and consumer behaviors, organizations simply do not possess the human resources to efficiently analyze such metrics without overlooking vital insights, thereby hindering a brand’s performance.
As is the case with keyword research and on-site optimization efforts, Visiture is looking into ways to more effectively deploy machine learning algorithms to shift through, analyze and extract meaningful, actionable insights from the copious amounts of metrics that flood in each day.
By Utilizing APIs and developing scripts that can effectively manage, harvest and interpret various data sets into readable reports, marketing teams at Visiture can obtain a more comprehensive understanding of how a site is performing in the SERPs, as well as how shoppers are interacting with on-site elements that results in better rankings and more conversions.
Search engines such as Google are employing AI and machine learning to better suit their user’s desires. Therefore, marketers and SEOs should be utilizing the same techniques to keep up with the times and evolve alongside engines like Google.
The technical nature of machine learning, along with the profound insights it can produce, make the technology a prime component to integrate into SEO efforts.
No matter if we are talking about finding the right keywords, optimizing on-site technical elements, generating better performing content offerings or analyzing how well a site reaches audiences and meets their needs, automation and machine learning technologies are likely to be the preeminent solution to all such optimization issues in the decade that follows.
As a result, eCommerce marketing agencies like Visiture align ourselves on the bleeding edge of such innovations to best serve our clients and obtain the most meaningful results possible.