Learn how to use machine learning for more precise, statistically relevant, and scalable SEO competitor research (with tools, code & more).

With the ever-increasing appetite of SEO professionals to learn Python, there’s never been a better or more exciting time to take advantage of machine learning’s (ML) capabilities and apply these to SEO.

This is especially true in your competitor research.

In this column, you’ll learn how machine learning helps address common challenges in SEO competitor research, how to set up and train your ML model, how to automate your analysis, and more.

Let’s do this!

Why We Need Machine Learning in SEO Competitor Research

Most if not all SEO pros working in competitive markets will analyze the SERPs and their business competitors to find out what it is their site is doing to achieve a higher rank.

Back in 2003, we used spreadsheets to collect data from SERPs, with columns representing different aspects of the competition such as the number of links to the home page, number of pages, etc.

In hindsight, the idea was right but the execution was hopeless due to the limitations of Excel in performing a statistically robust analysis in the short time required.

And if the limits of spreadsheets weren’t enough, the landscape has moved on quite a bit since then as we now have:

• Mobile SERPs.
• Social media.
• A much more sophisticated Google Search experience.
• Page Speed.
• Personalized search.
• Schema.
• Javascript frameworks and other new web technologies.

The above is by no means an exhaustive list of trends but serves to illustrate the ever-increasing range of factors that can explain the advantage of your higher-ranked competitors in Google.

Machine Learning in the SEO Context

Thankfully, with tools like Python/R, we’re no longer subject to the limits of spreadsheets. Python/R can handle millions to billions of rows of data.

If anything, the limit is the quality of data you can feed into your ML model and the intelligent questions you ask of your data.

As an SEO professional, you can make the decisive difference to your SEO campaign by cutting through the noise and using machine learning on competitor data to discover:

• Which ranking factors can best explain the differences in rankings between sites.
• What the winning benchmark is.
• How much a unit change in the factor is worth in terms of rank.

Like any (data) science endeavor, there are a number of questions to be answered before we can start coding.

What Type of ML Problem is Competitor Analysis?

ML solves a number of problems whether it’s categorizing things (classification) or predicting a continuous number (regression).

In our particular case, since the quality of a competitor’s SEO is denoted by its rank in Google, and that rank is a continuous number, then the ML problem is one of regression.

Outcome Metric

Given that we know the ML problem is one of regression, the outcome metric is rank. This makes sense for a number of reasons:

• Rank won’t suffer from seasonality; an ice cream brand’s rankings for searches on [ice cream] won’t depreciate because it’s winter, unlike the “users” metric.
• Competitor rank is third-party data and is available using commercial SEO tools, unlike their user traffic and conversions.

What Are the Features?

Knowing the outcome metric, we must now determine the independent variables or model inputs also known as features. The data types for the feature will vary, for example:

• First paint measured in seconds would be a numeric.
• Sentiment with the categories positive, neutral, and negative would be a factor.

Naturally, you want to cover as many meaningful features as possible including technical, content/UX, and offsite for the most comprehensive competitor research.

What Is the Math?

Given that rankings are numeric, and that we want to explain the difference in rank, then in mathematical terms:

rank ~ w_1*feature_1 + w_2*feature_2 + … + w_n*feature_n

~ (known as the “tilde”) means “explained by”

n being the nth feature

w is the weighting of the feature

Using Machine Learning to Uncover Competitor Secrets

With the answers to these questions in hand, we’re ready to see what secrets machine learning can reveal about your competition.

At this point, we will assume that your data (known in this example as “serps_data”) has been joined, transformed, cleaned, and is now ready for modeling.

As a minimum, this data will contain the Google rank and feature data you want to test.

For example, your columns could include:

• Google_rank.
• Page_speed.
• Sentiment.
• Flesch_kincaid_reading_ease.
• Amp_version_available.
• Site_depth.
• Internal_page_rank.
• Referring_comains count.
• avg_domain_authority_backlinks.
• title_keyword_string_distance.

Training Your ML Model

To train your model, we’re using XGBoost because it tends to deliver better results than other ML models.

Alternatives you may wish to trial in parallel are LightGBM (especially for much larger datasets), RandomForest, and Adaboost.

Try using the following Python code for XGBoost for your SERPs dataset:

# import the libraries

import xgboost as xgb

import pandas as pd

serps_data = pd.read_csv('serps_data.csv')

# set the model variables

# your SERPs data with everything but the google_rank column

serp_features = serps_data.drop(columns = ['Google_rank'])

# your SERPs data with just the google_rank column

rank_actual = serps_data.Google_rank

# Instantiate the model

serps_model = xgb.XGBRegressor(objective='reg:linear', random_state=1231)

# fit the model

serps_model.fit(serp_features, rank_actual)

# generate the model predictions

rank_pred = serps_model.predict(serp_features)

# evaluate the model accuracy

mse = mean_squared_error(rank_actual, rank_pred)

Note that the above is very basic. In a real client scenario, you’d want to trial a number of model algorithms on a training data sample (about 80% of the data), evaluate (using the remaining 20% data), and select the best model.

The post How to Use Machine Learning for SEO Competitor Research appeared first on Artificial Intelligence.

A Python library is a collection of useful functions and code. Learn how to use different Python libraries for SEO projects and tasks here.

Python libraries are a fun and accessible way to get started with learning and using Python for SEO.

A Python library is a collection of useful functions and code that allow you to complete a number of tasks without needing to write the code from scratch.

There are over 100,000 libraries available to use in Python, which can be used for functions from data analysis to creating video games.

In this article, you’ll find several different libraries I have used for completing SEO projects and tasks. All of them are beginner-friendly and you’ll find plenty of documentation and resources to help you get started.

Why Are Python Libraries Useful for SEO?

Each Python library contains functions and variables of all types (arrays, dictionaries, objects, etc.) which can be used to perform different tasks.

For SEO, for example, they can be used to automate certain things, predict outcomes, and provide intelligent insights.

It is possible to work with just vanilla Python, but libraries can be used to make tasks much easier and quicker to write and complete.

Python Libraries for SEO Tasks

There are a number of useful Python libraries for SEO tasks including data analysis, web scraping, and visualizing insights.

This is not an exhaustive list, but these are the libraries I find myself using the most for SEO purposes.

Pandas

Pandas is a Python library used for working with table data. It allows for high-level data manipulation where the key data structure is a DataFrame.

DataFrames are similar to Excel spreadsheets, however, they are not limited to row and byte limits and are also much faster and more efficient.

The best way to get started with Pandas is to take a simple CSV of data (a crawl of your website, for example) and save this within Python as a DataFrame.

Once you have this stored in Python, you can perform a number of different analysis tasks including aggregating, pivoting, and cleaning data.

For example, if I have a complete crawl of my website and want to extract only those pages that are indexable, I will use a built-in Pandas function to include only those URLs in my DataFrame.

Requests

The next library is called Requests and is used to make HTTP requests in Python.

Requests uses different request methods such as GET and POST to make a request, with the results being stored in Python.

One example of this in action is a simple GET request of URL, this will print out the status code of a page:

You can then use this result to create a decision-making function, where a 200 status code means the page is available but a 404 means the page is not found.

You can also use different requests such as headers, which display useful information about the page like the content type or how long it took to cache the response.

There is also the ability to simulate a specific user agent, such as Googlebot, in order to extract the response this specific bot will see when crawling the page.

Beautiful Soup

Beautiful Soup is a library used to extract data from HTML and XML files.

Fun fact: The BeautifulSoup library was actually named after the poem from Alice’s Adventures in Wonderland by Lewis Carroll.

As a library, BeautifulSoup is used to make sense of web files and is most often used for web scraping, as it can transform an HTML document into different Python objects.

For example, you can take a URL and use Beautiful Soup together with the Requests library to extract the title of the page.

Additionally, using the find_all method, BeautifulSoup enables you to extract certain elements from a page, such as all a href links on the page:

Putting Them Together

These three libraries can also be used together, with Requests used to make the HTTP request to the page we would like to use BeautifulSoup to extract information from.

We can then transform that raw data into a Pandas DataFrame to perform further analysis.

Matplotlib and Seaborn

Matplotlib and Seaborn are two Python libraries used for creating visualizations.

Matplotlib allows you to create a number of different data visualizations such as bar charts, line graphs, histograms, and even heatmaps.

For example, if I wanted to take some Google Trends data to display the queries with the most popularity over a period of 30 days, I could create a bar chart in Matplotlib to visualize all of these.

Seaborn, which is built upon Matplotlib, provides even more visualization patterns such as scatterplots, box plots, and violin plots in addition to line and bar graphs.

It differs slightly from Matplotlib as it uses fewer syntax and has built-in default themes.

One way I’ve used Seaborn is to create line graphs in order to visualize log file hits to certain segments of a website over time.

This particular example takes data from a pivot table, which I was able to create in Python using the Pandas library, and is another way these libraries work together to create an easy-to-understand picture from the data.

Advertools

Advertools is a library created by Elias Dabbas that can be used to help manage, understand, and make decisions based on the data we have as SEO professionals and digital marketers.

Sitemap Analysis

This library allows you to perform a number of different tasks such as downloading, parsing, and analyzing XML Sitemaps to extract patterns or analyze how often content is added or changed.

Robots.txt Analysis

Another interesting thing you can do with this library is to use a function to extract a website’s robots.txt into a DataFrame, in order to easily understand and analyze the rules set.

You can also run a test within the library in order to check whether a particular user-agent is able to fetch certain URLs or folder paths.

URL Analysis

Advertools also enables you to parse and analyze URLs in order to extract information and better understand analytics, SERP, and crawl data for certain sets of URLs.

You can also split URLs using the library to determine things such as the HTTP scheme being used, the main path, additional parameters, and query strings.

Selenium

Selenium is a Python library that is generally used for automation purposes. The most common use case is testing web applications.

One popular example of Selenium automating a flow is a script that opens a browser and performs a number of different steps in a defined sequence such as filling in forms or clicking certain buttons.

Selenium employs the same principle as is used in the Requests library that we covered earlier.

However, it will not only send the request and wait for the response but also render the webpage that is being requested.

To get started with Selenium, you will need a WebDriver in order to make the interactions with the browser.

Each browser has its own WebDriver; Chrome has ChromeDriver and Firefox has GeckoDriver, for example.

These are easy to download and set-up with your Python code. Here is a useful article explaining the setup process, with an example project.

Scrapy

The final library I wanted to cover in this article is Scrapy.

While we can use the Requests module to crawl and extract internal data from a webpage, in order to pass that data and extract useful insights we also need to combine it with BeautifulSoup.

Scrapy essentially allows you to do both of these in one library.

Scrapy is also considerably faster and more powerful, completes requests to crawl, extracts and parses data in a set sequence, and allows you to shield the data.

Within Scrapy, you can define a number of instructions such as the name of the domain you would like to crawl, the start URL, and certain page folders the spider is allowed or not allowed to crawl.

Scrapy can be used to extract all of the links on a certain page and store them in an output file, for example.

You can take this one step further and follow the links found on a webpage to extract information from all the pages which are being linked to from the start URL, kind of like a small-scale replication of Google finding and following links on a page.

Final Thoughts

As Hamlet Batista always said, “the best way to learn is by doing.”

I hope that discovering some of the libraries available has inspired you to get started with learning Python, or to deepen your knowledge.

Python Contributions from the SEO Industry

Hamlet also loved sharing resources and projects from those in the Python SEO community. To honor his passion for encouraging others, I wanted to share some of the amazing things I have seen from the community.

As a wonderful tribute to Hamlet and the SEO Python community he helped to cultivate, Charly Wargnier has created SEO Pythonistas to collect contributions of the amazing Python projects those in the SEO community have created.

Hamlet’s priceless contributions to the SEO Community are featured.

Moshe Ma-yafit created a super cool script for log file analysis, and in this post explains how the script works. The visualizations it is able to display including Google Bot Hits By Device, Daily Hits by Response Code, Response Code % Total, and more.

Koray Tuğberk GÜBÜR is currently working on a Sitemap Health Checker. He also hosted a RankSense webinar with Elias Dabbas where he shared a script that records SERPs and Analyses Algorithms.

It essentially records SERPs with regular time differences, and you can crawl all the landing pages, blend data and create some correlations.

John McAlpin wrote an article detailing how you can use Python and Data Studio to spy on your competitors.

JC Chouinard wrote a complete guide to using the Reddit API. With this, you can perform things such as extracting data from Reddit and posting to a Subreddit.

Rob May is working on a new GSC analysis tool and building a few new domain/real sites in Wix to measure against its higher-end WordPress competitor while documenting it.

Masaki Okazawa also shared a script that analyzes Google Search Console Data with Python.

The post 8 Python Libraries for SEO & How To Use Them appeared first on Artificial Intelligence.

Learn how data science can evaluate, predict, automate, or even influence different tasks or phenomena in SEO

Have you ever looked at SERPs, keyword trends, and behavior on your website, and wondered if you couldn’t predict how adding new pages would impact how often organic visitors from your website are converted to leads?

Have you ever wondered if you could pick up on industry trends in SERPs before your competitors?

Have you taken a good look at your server logs in and asked yourself what Googlebot browsing patterns reveal about your website’s structure, pertinence, and technologies?

These questions, which bring together different types of data used in SEO strategy, are all part of data SEO. Data SEO is the rising field that brings data science methods of analysis to SEO. Data science uses scientific principles, statistics, and machine learning to bring together and analyze digital data for the purpose of decision making.

The post How data science can change the way you look at SEO appeared first on Artificial Intelligence.

Artificial intelligence (AI) is becoming a big part of our lives and is capable of doing the various task, generating convincing text is one of them. Though there are many concerns regarding it but tricking Google is prominent than any other concern. 

Numerous blogs, website and marketing spam content can be generated through AI along with keywords embedded in it. The content produced this way is more economical but would not be much meaningful. Still, the search engine would not be able to differentiate it from original content. 

Content marketing agency, Fractl showed how AI text generation can be implemented on a large scale for SEO (Search Engine Optimization) Business. 

Fractl partner, Kristin Tynski said that AI is capable of producing a massive amount of content which is hard for humans and search engines to discriminate. 

Grover, an open source tool developed by Allen Institute for Artificial Intelligence is used by Fractl to write blogs. Tynski said that though the company does not use AI to serve its client competitors are most likely to use it for their advantage. 

Tynski explained in a blog post on SEMrush that how almost 10 to 15 years ago a trend of article spinning started. By using tools, the words of original content were changed to make it look like a new piece of writing. Over time, Google and other search engines worked to differentiate the copied content with a lot of filters.

According to her, AI-generated text will be a lot better than spinning articles and will be capable of generating massive content in all possible niches. 

An SEO expert, Mike Blumenthal said that spammers will use these AI tools at a huge level, especially considering how massive content is produced at cheap cost. To generate traffic, many news merchants may create fake articles. 

Rowan Zeller, who created the Grover tool said that it is possible to detect AI-generated text, particularly at this stage. He along with his team already generated a system that was capable of identifying Grover-generated text, showing 92% accuracy. 

Zeller is of the view that AI-generated text has various linguistic and grammatical errors and thus can easily be identified. Instead of forming new phrases or nouns, it uses the same patterns. 

Even if a technology is developed to identify it, it has the potential to cause harm. It is not easy to integrate detectors in internet infrastructure. Also, the detectors can also make numerous mistakes and there is no guarantee of complete accuracy of detecting AI-generated content. 

Google did not comment on this issue but clarified that it always has and will continue fighting spam. 

Proxies like Siri and Alexa are used for web searches and search engines like Google have to show around 3 – 4 more relevant answers. Although, fewer answers may not be enough but they decrease the chances of spamming. 

AI text generation is improving rapidly and is expected to set benchmarks. A program that can create content like humans, it will not be a surprise to see it as an AI assistant. 

Tynski said that it is possible that in coming years, technology will be so advanced that it will be able to create the content of the level of humans. Once the human-accuracy achieved, it could give humans a tough time, as to why bother humans when there are systems to do tasks.

The post AI-Generated Stories can Influence Google Search Results Immensely appeared first on Artificial Intelligence.

Tools that facilitate (and accelerate) work on-site optimization and make the content better, more precisely and better marked are of increasing importance. Such tools and services are mostly based on artificial intelligence, and on this occasion, we investigate how SEO can affect SEO optimization.

Since Google is still the most popular search engine in the world, SEO experts are mostly guided by the latest solutions and changes coming from that company.

Artificial Intelligence (AI), which this technological giant uses, probably exceeds our most optimistic ideas. Although Google has never fully discovered how the algorithm works, something is still known to us. We know, for example, that the so-called Google RankBrain uses machine learning in order to present users with the most relevant and high-quality search results.

We also know that Google has announced a far greater significance of visual content in searches, especially images and videos, as well as moving from simple responses to tracking the so-called. Search path, i.e. the time when users pass, for example, from the initial interest for a particular product to its ordering or online purchase.

We present some important ways to use AI to improve SEO optimization.

Well, let’s start.

Code validation

Considering that the unauthorized code on the site is considered one of the SEO errors that occur during the creation of the site, it is clear that the validation of the source code should be taken into account. Some of the tools for controlling and correcting HTML code are based on algorithms today that border with AI when it comes to analyzing HTML tags.

These tools indicate whether the site is valid or that it complies with World Wide Wed Consortium (W3C) standards and warns of errors. As a result, of course, it generates a much cleaner and more quality code.

Why Is It Important For SEO?

If you want Google and other search engines to successfully index content on your site, it’s important that you validate the code for each page of the site. Otherwise, you can lose credibility with Google due to poor coding practices, which can easily lead to a drop in position when ranking in search.

Domain authority 2.0

Different parameters are used daily in site optimization. Several companies who are considered leaders in the SEO industry, including the leading Moz, Ahrefs, and Majestic, have been developing and continually improving their algorithms for calculating domain authority, the authority of individual pages, indicators of the likelihood of ranking domain searches and many others for decades.

Domain Authority (DA) is one of the most well-known and most popular metrics that point to the assessment of the quality of one domain and the likelihood that the site will be better ranked in search compared to others, primarily on the competition. Developed by Moz company, it is calculated algorithmically and is expressed in values from 0 to 100, where the higher digit shows a higher quality domain.

Recently, the algorithm has been improved and now, in its application, artificial intelligence and machine learning are becoming more apparent.

Domain Authority 2.0, as it was officially called up by Moz, is tasked with counting domains manipulated in sales bids, as well as those who are evidently dealing with the sale and purchase of backlinks or applying some of the black hat SEO techniques.

Many people (wrongly) think that domain authority is one of the factors affecting search results on Google. It’s not. But, as Moz explained recently, YES in a large percentage accurately predicts the potential of a site for ranking. An improved version now makes it even more precise.

How Does It Affect SEO?

The new DA is based on a smart neural mechanism that recognizes when a site manipulates links in some way or uses undesirable SEO techniques and therefore punishes it with lower authority.

Due to the launch of the new DA in February 2019, almost all sites had a fall in authority. Regular sites, as they pointed out in Moz, experienced a 6% drop on average, while those who manipulate links, resell domains at auctions, and deal with unsuccessful actions facing the decline of DA, reaching as much as 98%.

Artificial intelligence, in this case, contributes to a much fairer Internet, but also to more quality search results that are presented to users.

Improved image search results

Considering that Google has announced a greater significance of visual content in search, and a new way to rank photos and videos in search, this browser now uses the AI for the intelligent design of the AMP story, as part of an open source library that anyone can use to create own content.

As explains Cathy Edwards, the director of the engineering department at Google, that’s exactly what will be better positioned in the search. At the end of 2018, it announced several new features that will help users easily find visual information and successfully perform a search where it’s important to have a visual reference.

In addition, sites that contain a large number of photos and other visual content, most often use AI tools that perform automatic content recognition and tagging.

How does artificial intelligence know that the picture is, say, wood? By analyzing shapes and edges, it compares contours with already existing tree images in its base, then it comes to a calculation in which percentage of the contour and color of the particular picture coincide with what is in the base.

What does this mean in practice?

If it turns out that there is, for example, 60% of the probability of the tree, then it is estimated whether it is the right tree or not, according to the parameters set in a particular place.

In other words, if you are asked to confirm what was discovered with security greater than 80%, in this particular case, the tree would not be designated as one of the elements in the picture (because the probability is 60%).

Why Is It Important For SEO?

Optimizing images and video content has always been very important for SEO optimization. Today, however, the use of AI in image marking tools makes it easier for professionals to work so that they may not soon need a human hand for such an action.

This speeds up the job, but it should still be taken into account and subsequently controlled by each AI result since it can be defective. Paradoxically, these are usually mistaken that people would not otherwise make.

Neural synchronism matching

When it comes to synonyms, the most significant is the latent semantic indexing method (latent semantic indexing, LSI). This is a method designed to improve the accuracy, accuracy, and relevance of the information returned by the search engine after the query.

However, Google began using neural matching to better understand synonyms. This AI method helps to connect concepts with words. As Danny Sullivan, a search engine at Google officially explained, a neural matching affects 30% of search queries.

This technique was also mentioned at events on the occasion of the 20th anniversary of Google when it was pointed out that artificial neural networks help turn words into a representation of basic concepts and then compare them with concepts in query and concepts in results.

What does SEO mean?

This means that keywords do not have the same importance in optimization in themselves. Much more attention should be paid to the entire context of the content, the concept on which the content is based and the use of as many sanitizers as possible to help search engines determine the relevance for a specific query.

The post Artificial intelligence and SEO optimization appeared first on Artificial Intelligence.

Achieving a top ranking on Google has been the utmost priority of SEO experts. Google is constantly changing the way web pages are ranked on its search result pages. To stay more unpredictable, yet relevant, to avoid getting gamed by thousands of SEO experts worldwide, Google is actively adopting new-age technologies the future belongs to. And, it’s Artificial Intelligence, aka AI, Google is relying on the most.

But, it’s not the AI alone that is changing the way things work with Google Search; The exploded adoption of high-speed internet connectivity has given wings to users to get surrounded with devices that are operated through voice commands. Hence, Voice search is also playing an important role in shifting the ways people use Google Search.

So, does it mean that all the tactics of Search Engine Optimisation, aka SEO, hundreds of thousands of SEO experts are practising worldwide have become null and void?

While the straight answer to the question is very difficult, and controversial as well, the easiest way to understand is, No, It’s not dead. Albeit changed completely!

We are entering into a new era of a real-time ranking of web pages on Search Engines. Gone are the days when SEO gurus were confident about their traditional old practices that got clients’ websites ranked among the top ones on Google, and, more importantly, maintained their position for months.

With the help of Artificial Intelligence (AI) Google is able to analyze the context of content in a better manner and serve the result pages more accurately. As accurate as 89% of Google search engine users don’t explore results beyond the first page. Thanks to RankBrain that helps Google to understand users’ intent behind the query and plays an important role in generating impeccable responses to search queries.

RankBrain: The Secret Sauce Behind Top Ranking On Google?

In the last one decade, the exploded adoption of mobile and internet has resulted in a huge surge in the number of search queries Google attends with each passing second. Traditional methods of writing algorithm were failing fast to match the expectations, as it used to follow a strict set of rules set by humans (developers). The biggest challenge for Google, since long, has always been to keep the ranking mechanism unpredictable as much as possible. It was the time when Amit Singhal, the former Senior Vice President of Google, decided to go all-in on Artificial Intelligence. In early 2015, Google started rolling out a deep learning system that helps generate more accurate results to search queries. Google calls it RankBrain. Within a year of the major rollout, it started processing millions of search queries that are submitted by users to Google every second.

… RankBrain uses artificial intelligence to embed vast amounts of written language into mathematical entities — called vectors — that the computer can understand. If RankBrain sees a word or phrase it isn’t familiar with, the machine can make a guess as to what words or phrases might have a similar meaning and filter the result accordingly, making it more effective at handling never-before-seen search queries. …” explained Greg Corrado, from Bloomberg.

So, does it mean that RankBrain has taken over all the algorithm and mechanism that were followed by Google to rank a page?

No, it has not taken over completely. But it’s among one of the most important elements that decide whether a web page is perceived as relevant, and included in SERPs for a query. In a simple world, again, whether your web pages would be listed on Google for particular search queries or not.

Not officially though, it’s been said that Google captures over 200 signals to rank a particular page on its result pages. Each signal in the hierarchy is listed based upon its importance.

RankBrain is the third most important signals in the Google search algorithm and it was revealed by Google’s Andrey Lipattsev itself.

It’s important to understand here that RankBank is not a typical SEO factor. It’s just one of the influential factors.

Hence, it’s safe to say that Artifical Intelligence has raised the bar for the SEO and the brands need to adapt to stay competitive in the market.

Companies offerings SEO services need AI-powered automation to audit site content, resolve errors and suggest new optimizations on an ongoing basis, based on real-time data, and consumer behaviours. They need smart monitoring to identify content that is outranking their client’s site. And, still, all of these are just a small piece of efforts required to compete with AI-Powered ranking mechanism employed by Google.

In the last few years, Seach Engine Optimisation practices are redefined. Besides going on-page and off-page optimisation, there are hundreds of other factors that need to be taken care of to have a web page ranked at the top position. Nowadays we need to spend more time analyzing a web page and researching about the competing pages before we can show some progress to our client on grounds. And’s that’s impossible without having AI-powered tools.” says Bala Kumaran, Founder –  BrandStory, one of the leading SEO companies in Bangalore, India.

Bala strongly suggests that companies offering an array of digital services nowadays must understand the interlinking of digital services with SEO.

“Be it on-page content creation to influence RankBrain or back lining to influence SERP, a company must have end-to-end offerings related to Digital services to compete with ever-changing AI-powered ranking mechanism of Google,” adds Bala.

In 2017, an interesting survey by SEJ revealed that nearly 55% of respondents believed that AI will take over Google Search completely in the next 10 years. And, there is no way the world could stop it. However, it’s may be too early to say that AI has killed SEO.

SEO is changed for sure. It’s even tricker, time-consuming, and evolving much faster than before. Google is, apparently, changing its algorithm 500 – 600 times in a year. And, that’s why SEO experts cannot afford to be late adopters in the AI arena and laggards in relation to real-time SEO. Today, they need to be able to anticipate searcher needs and have technology in place to trigger the optimizations that will return their clients’ content as the top answer for relevant queries.

In nutshell, SEO experts need adaptable, full-proof SEO strategy for 2019 and beyond. and, it’s high time when brands need to choose their SEO partners wisely, probably with the help of AI.

The post Top Ranking On Google: Has AI Killed SEO? appeared first on Artificial Intelligence.

Understanding SEO is the first rule for being able to optimize your site and its content so that would-be customers can actually find your company online. When your SEO is on point, you are more likely appear in search engine results when someone looks for the type of products or services you sell. However, to be able to truly understand SEO, you need to comprehend how modern search engines work — and that means understanding artificial intelligence, or AI.

What is AI?

AI is a technological advancement that enables a combination of hardware and software to function like a human brain — minus the inherent flaws in logic and the relatively small memory capacity. It makes it possible to not only analyze large amounts of data but to draw meaningful insights about the information. In some cases, these AI-enabled insights are more insightful, quicker or on a much larger scale than would be possible with human involvement — usually all three.

And those insights can help build better search engine results. “If you feed enough photos of a platypus into a neural net, it can learn to identify a platypus,” writes Cade Metz, formerly of Wired. “If you show it enough computer malware code, it can learn to recognize a virus. If you give it enough raw language — words or phrases that people might type into a search engine — it can learn to understand search queries and help respond to them.” In other words, AI learns over time.

In general, there are three types of artificial intelligence:

1. Artificial Narrow Intelligence (ANI): This type of AI focuses on one function (e.g. chess, spam filters).
2. Artificial General Intelligence (AGI): When AI has a general, overall function. Usually, this is equated to the ability of a single human.
3. Artificial Superintelligence (ASI): This is AI that is beyond Artificial General Intelligence. The neural network operates at a much higher capability than AGI.

Google’s RankBrain is a type of ANI. Specifically, it is a connection-based system that mimics the way people learn. Also called deep learning, connectionist ANI uses back-propagation to identify flaws in the output of the system (e.g. misidentifying a duck as a platypus) and uses that information to inform future query results.

Google RankBrain (and how it differs from previous updates).

Google RankBrain is the current leader. The rollout of RankBrain began in 2015. It is a search engine system that uses artificial intelligence to create more relevant responses to search engine queries. Previously, Google’s focus had been on algorithms designed by humans.

It took time, but RankBrain slowly started to replace the use of algorithm-based technologies. Today, it is the prevailing search engine technology, and it is changing everything.

Marketing is already shifting directions.

Already, RankBrain has changed SEO, but the result has not been linear. The algorithms that Google used before still exist. What has changed is the way those methods are applied. RankBrain uses deep learning to figure out which combination of those algorithms will produce the best search engine results.

“For instance, in certain search results, RankBrain might learn that the most important signal is the META title. Adding more significance to the META title matching algorithm might lead to a better searcher experience,” explains John Rampton, founder of the online invoicing company Due. “But in another search result, this very same signal might have a horrible correlation with a good searcher experience. So in that other vertical, another algorithm, maybe PageRank, might be promoted more.”

The exact algorithm mixture varies considerably each time. RankBrain isn’t trying to figure out which combination is best, per se. It is determining which sites are good and bad.

What makes a site good.

RankBrain learns which sites answer search engine queries best and analyzes them for common features. This includes the keywords used and the density of those words, but that’s just scratching the surface. RankBrain also looks at the structure of the site. For instance, if a website is overly generalized, it could flag as “bad” and be ranked lower than a more niche site. RankBrain also looks at the backlinks. For example, a shoe website linking to a clothing website is perfectly normal but linking to a computer website? Not so much.

To avoid misclassification, you need to follow the parameters that RankBrain has found to identify “good” sites.

Adapting to the new normal.

Good sites are becoming the new normal, and that fact is changing the way we SEO. Sticking to a niche and using quality backlinks helps, and that’s just the beginning. You can adapt to the new normal in SEO marketing by understanding how RankBrain works.

Prioritizing quality.

Start by prioritizing quality. Whether the issue is graphics or content, quality matters. It creates more engagements amongst users and earns more links amongst your peers. RankBrain pays attention to this information and adjusts your search engine ranking accordingly.

User experience.

User experience also matters. Ease of navigation, quality graphics and overall usability make a real difference. RankBrain isn’t going to rank a “crummy” site very high. Having a similar structure to the sites that leaders in your industry have helps.

Relevant content experience.

Content relevance counts too. When someone comes to your site to buy a sweater, you shouldn’t send them to a blog post about Fall trends. Likewise, links should be anchored in relevant keywords and connect to sites that make sense with those anchors.

SEO is becoming a game of cat and mouse, with Google constantly changing the game. AI and, by extension, big data is a huge part of that. Help your company stay on top by adapting to the new normal in SEO.

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Smartphones have forever changed the way businesses reach out to us and have given rise to an inbound marketing movement. What does this new technology mean for the consumer? Well, it’s all good news, to be honest. Let’s take a look at how the evolution of smartphone artificial intelligence (AI) will change the lives of consumers.

Businesses Intelligence Needs To Match Smartphone AI

A few years ago, ordering online was like ordering through a catalog. You would see something online you like, then most likely try to find it in an actual storefront somewhere. Now, the shifts in procurement and delivery have made online buying easier than going to the store.

Shipping charges are now lower and that small fee is worth it for the convenience of not having to trek all over town to find the item you’re looking for. It’s here on your screen now, so you pull the trigger.

The Battle For Longtail Keywords

Today, longtail keywords being asked to Siri or Google are the most buy-ready and intent-based product inquiries we’ve ever seen. Businesses are now in an all-out war to come up with the solutions that customers need when they are asking these questions.

The businesses that are succeeding today are building their entire approach around these keywords. They’re using longtail- focused search engine optimization (SEO) practices to show up in search engines and creating a user experience that guides the customer from information to purchasing as quickly as possible. They’re also investing in conversion rate optimization (CRO) to make sure they’re converting the highest number of customers possible and re-marketing campaigns to try to win back leads they have “leaked.”

A Mobile-First World

Advances in smartphone AI have pushed the global traffic over to a now predominantly mobile world. Worldwide mobile traffic is now higher than desktop traffic. This led Google to announce that they will now be moving towards mobile-first indexing. This means your mobile presence will now drive your SEO and CRO success, as well as your bottom line.

Your mobile site has to impress Google, but then it has to impress the user. A Google study revealed that 61% of your customer will leave your mobile site if there are any problems or delay, and 40% said their next stop will be your competition’s site.

Businesses who adopt a mobile-first and user experience driven mentality will win the war. The rest won’t survive.

Intent-Based Marketing

Intent-based marketing is a major trend in the world of digital marketing being pushed forward by smartphone AI. Ask yourself, do you really know what your customers want? Most specifically, do you know what they want to see and find when they land on your mobile site? Probably not. Most businesses think they know, but they’re wrong.

Their conversion rate suffers because they have yet to figure out their target audience’s true intent when they load a page. Then the user leaves, unfulfilled and disappointed. The competition now gets that lead, and maybe the sale.

Demographics are good, but they don’t tell you the whole story. You need to understand the intent to see the big picture. In fact, Google has said that marketers who rely only on demographics to reach consumers risk missing more than 70% of potential mobile shoppers.

It’s such a simple concept: Give the people the exact thing that they want, at the exact second when they’re looking at their phone to buy it. However, understanding that intent is incredibly complex.

Master Micromoments

How many leads are you losing to micromoments? Probably way more than you know. A micromoment is the brief half a second where a user has an intent to pull the trigger on a purchase from their phone. They can either feel compelled to hit “buy” or be dissuaded by one of two major things: their own objections (now isn’t really the time, I can’t really afford this) or a  breakdown in user experience (the page isn’t clear, takes too long to load or has too many steps).

How do you make sure you don’t lose these leads? Pay attention to the little things.

Microcopy Preserves The Micromoment

You have a very small screen and a limited amount of space to work with, so make every word count. Focus on microscopy. These are the little tiny words and phrases on your page that most businesses don’t give a second thought to.

In a world of increasing smartphone AI, you need to use this space to address and handle any possible objections that your would-be buyer has in that split second.

You can send your conversion rates skyward by using some variation of these phrases right above or below your “buy” button.

• You don’t need a credit card to buy/register

• You don’t need to create an account to buy/ register

• You can cancel at any time

Businesses should also use this space to add a bit of social proof,or reassurance that this buyer is about to make a great decision. Some little bits of microcopy can help sell this. If you have an endorsement, testimonial or award, now is the best time to make that clear to the customer.

Also, be very mindful of the words you put on the button itself. Avoid the word “submit” altogether. People hate it and it kills conversions. Also avoid words like “register” that may come off as too much of a commitment, if your user just wants to make a simple buy.

Matching business intelligence to smartphone AI is key to business growth. This means that you must understand your customer and also get to them at that very moment when they want to purchase something. Achieving these goals is complex. If you understand how AI plays a pivotal role in the process, however, you will understand the need to integrate it into your business sooner rather than later.

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