Search engine marketing has gradually undergone significant changes in correlation to consumer preferences and advancements in technology. One of the most disruptive technological innovations is the evolution and application of artificial intelligence.
Remember when search engine optimization involved stuffing keywords at the bottom of your web page so that search engines would deem the quality as more relevant for readers? As you can probably guess, this didn’t prioritize the most valuable content for readers. Although Google’s search algorithm updates took care of this inefficiency, it still took them a while.
Artificial intelligence, on the other hand, prioritizes the searcher’s intent and would primarily adjust based on results the searcher wants to see. This has made for a better searching experience and is going to transform the way the search engine marketing industry operates.
What Is Artificial Intelligence?
Artificial intelligence is often thrown around as a buzzword to describe the ability for computers to be programmed to learn without further instruction. Although not inaccurate, artificial intelligence as we know it is in its embryonic stages and what we are familiar with is actually machine learning.
Machine learning evolved from the study of a subsection of artificial intelligence that has to do with pattern recognition and computational learning theory. When applied to search engine marketing, machine learning helps search algorithms make valuable sense of uncovered patterns to provide better results.
Search engine algorithms were primarily static, and machine learning allows them to function in a more dynamic setting that becomes more effective (and harder to manipulate) as time progresses and more data is collected. Machine learning allows search engine algorithms to make data-driven decisions and predictions.
What makes machine learning incredibly impactful in search engine marketing is the fact that it doesn’t necessarily need supervision to function. Since it’s not static, it becomes much harder for a scrupulous search engine marketer to take advantage of inefficiencies in the algorithm.
Search Engine Algorithms and Artificial Intelligence
The search engine optimization industry is built around the ability to learn and find patterns in search engine algorithms to help send traffic to a specific site. Countless hours are spent doing keyword research, optimizing on-site content, creating informative and valuable content, and digging into meta elements. All the time and effort spent learning how search engines work and how best to use them for marketing could change as artificial intelligence progresses.
The Holy Grail for any SEO is learning the elusive algorithm Google uses to show results; however, this algorithm might be doing a better job at learning than we are. Algorithms are the foundational structure of search engines, but they have the enormous challenge of being able to match the conversational and semantic level human beings use to communicate.
There are upward of thirty trillion pages indexed by Google, amounting to 100 million gigabytes. Google searches this seemingly infinite pool of data over 100 billion times a month. To make matters even more impressive, Google is able to deliver a page full of relevant results in an eighth of a second onto your computer or the smartphone in the palm of your hand.
A Google search for “bald guy fast and furious” will bring up a series of results pertaining to The Fast and the Furious movie series, character bios, and some images of Vin Diesel. While this may seem like a fairly simple topic to ask another human being, search engines approach it with a much higher complexity. The early search engines would have shown content with the keywords ‘bald guy fast and furious” or whatever was closest, as opposed to specific laid-out information for the question.
Additionally, search engines must understand the relationship of everything in the query. They must know that the words “fast and furious” go together, and this is the name of a movie, that “bald guy” is a separate item in the search, and that they must find the relationship between both of those strings. It would not be incorrect to assume Google wants to take their algorithm as close to the realm of personal communication as possible, and they are going to use machine learning to study the search and click patterns of its users to do so.
Google was founded almost 20 years ago and has been working on perfecting their search algorithm ever since. Sundar Pichai, CEO of Google, claims that they are “betting big” on machine learning and have already started implementing certain machine learning components.
Google’s ranking procedure primarily uses over 200 secret factors that take into account quality of the website, age of domain, safety of the content, freshness of results, user context, prior searches, Google+ connections, and history. As machine learning continues to evolve in Google, it will make all of these factors more efficient at sorting out what will, ultimately, provide searchers the most value.
SEARCHERS WILL BE PROVIDED WITH MORE ACCURATE SEARCH RESULTS
Since machine learning primarily operates on analyzing and adjusting to patterns, with enough data points it will be able to provide users with more accurate search results. This means search engine marketers will have to provide the best possible content and will have to rely on people searching on Google to validate the content as worthy. This is ultimately about helping Google’s user experience and connecting people based on their search intent, not so much allowing marketers to push their content to capitalize on search volume.
Search marketers who publish high-quality content that provides value and solves problems for Google’s users will be rewarded, whereas sites that have low-quality content will likely be relegated to lower ranking spots.
Up until recently, search engine crawlers didn’t know much about the images on a certain page. Search engine marketers got around this by making specific meta-data and alt-text and naming the image file to help search engines understand what was on the page, but this has its limits.
Machine learning has already enabled people to search for similar images in Google, and this is going to have massive applications for the search engine marketing industry. There are software platforms that allow eCommerce websites to optimize their images for visual search. Consumers are essentially able to upload an image of a product and find similar or complementary products purely based on the image. Human beings are visual creatures, and these advancements are expected to tailor to our visual preferences.
This is done by advanced visual search capabilities that scan the item for specific clues such as size, color, and shape. Customers will be able to upload a particular item, and search engines will even dive into specificities such as the fabric and brand to bring up relevant search results. This makes it easier for customers to find exactly what they’re looking for.
While things may get easier for searchers, these updates could cause some trouble on the other end of the search industry. SEOs and search engine marketers primarily rely on text-heavy content to draw in traffic and, once image-heavy search starts to become more common, they will have to readjust their strategies.
Search engine marketers, particularly those involved in eCommerce, will have to place a higher emphasis on their image optimization in order to meet the demands of consumers searching based on images.
PERSONALIZATION LIKE NEVER BEFORE
Personalization in search engine marketing isn’t a novel idea. Artificial intelligence has the ability to change the way online brands personalize recommendations and search results to a highly focused specificity.
You’re probably familiar with a “Buyers who liked this also bought …” sort of pop-up window on Amazon and other eCommerce sites. These recommendations are mostly based on how traffic interacted with that initial page and other activities it may have done in that time period. The filters are also based on most viewed history, trends, best sellers, and other nonspecific parameters.
What allows artificial intelligence to enhance personalization components is because, unlike the current collaborative filters, it is not limited to only one channel. The current filters are mostly limited to a single online store, a brick and mortar location, or another mobile application. Although the current filters do a great job at recommending items, the algorithms behind them require a lot of data that must be analyzed for a proper personalization.
Artificial intelligence, on the other hand, has the ability to create a seamless customer experience across multiple channels while inputting new signals every time they get uncovered. This will allow search engine marketers the ability to create extremely specific personalization options.
CUSTOMER EXPECTATIONS WILL INCREASE
Artificial intelligence has the potential to completely change how customers interact with search engines, as well as significantly raise their expectations. The theory behind implementing deep learning into search engines focuses on creating a better, more efficient consumer experience.
For search engine behemoths such as Google, the customer experience is the most important part of their business. Without people actively searching and getting what they want, there will be no one to click on ads or purchase products.
Google’s algorithm updates are known to be merciless toward search engine marketers that are abusing their current system and not providing users an amazing search experience. As machine learning continues to advance, it will continue to prioritize the best content on the internet for each single query.
Following this logic, one can expect a higher emphasis on semantic keyword research, creating extremely valuable on-site content, and leveraging other methods of getting traffic to sustainably boost your search engine optimization tactics.
Therefore, Google is going to use artificial intelligence to the best of its ability to set higher customer expectations, and digital marketers that are not able to meet these new expectations will likely have their pages fall into the oblivion rankings.
A majority of users are also positively receptive to artificial intelligence. 70% of Millennials in the United States noted that they would appreciate online brands using artificial intelligence to suggest more interesting products. This appreciation comes from the basis of being shown advertisements, products, and information that they want, as opposed to poorly executed ad campaigns.
THE RISE OF VIRTUAL SHOPPING
A little over a decade ago, the concept of a personal shopper was a human being who had somewhat of an understanding of your personal tastes and preferences with a vague idea of what you wanted, and who went to a shopping mall to pick up a variety of items you might want.
Artificial intelligence has essentially transformed personal shoppers into the virtual arena. Virtual shopping caters to both shoppers that love shopping and want a better shopping experience and shoppers that would rather save time.
There are several subscription services, like Birchbox or Trunk Club, that curate scheduled packages of assorted items or clothing, but artificial intelligence poses a looming threat. Since artificial intelligence gathers as much information about specific users along with data pertaining to thousands of similar shoppers, it has the potential to completely change the way shoppers use search engines to get what they want.
Additionally, virtual shopping powered by machine learning does not need further instruction and, technically, becomes better based on whether the user accepts or rejects certain options. Each data point gets stored and immediately reflected upon to bring up another item that may or may not appeal to the user. These AI shopping assistants can analyze massive amounts of data in seconds, can create human-like interactions to reflect a brand’s image, and are available on demand.
HIGHER RATES FOR BETTER PAID SEARCH
In 2015, Google revealed that mobile search passed desktop search for the first time. The implications concerning artificial intelligence stemming from this meant that, since phones have less screen space and there is less room for paid and organic search options, artificial intelligence will have to determine which results are worthy of belonging in this much more competitive real estate.
AI for SEM PPC –Machine learning has the potential to find higher converting traffic than traditional search engine algorithms. Traditional search engines primarily operate on specific keywords and queries, some of which tend to attract customers who have a higher potential to convert. PPC ads for these specific keywords are usually more expensive since they have a higher possibility of earning revenue.
This works fairly well today, but artificial intelligence has the possibility to further qualify these keywords based on a much wider range of variables. Instead of traffic being delivered from a certain keyword, a much higher amount of qualifying factors—including prior history to even, potentially, a user’s standard conversion rate—can be included. This is expected to make paid search much more effective, and, since conversion rates increase, the costs for paid search advertising will increase as well.
AI for Organic SEM –While organic SEO likely isn’t going to be linked to the same sort of payment structure as PPC, the bar is going to be raised for the content that is going to be displayed on a search engine results page. Traditionally, search engine optimization revolves around creating content that targets one or multiple keywords.
Artificial intelligence will likely include a much more involved approach that goes beyond a simple keyword. The content that best answers the questions and provides value to users is still going to float to the top, but, as search engine algorithms become much more powerful at providing users with exactly what they are looking for, the competition for this is going to increase as well.
SPOKEN WORD SEARCH QUERIES
Artificial intelligence provides the possibility of collaboration and integration of SEM with everything, from Internet of Things applications to social media.
Stuart Frenkel, CEO of Narrative Science, noted, “Search engines like Google and Bing have already made big moves enabling search queries via spoken word while Facebook launched an AI-effort, DeepText, to understand individual users’ conversational patterns and interests. Meanwhile, the move toward natural language interfaces has already picked up steam with the explosion of companies focused on enabling chatbots, digital assistants and even messaging apps eclipsing social networks in monthly activity. ”
In the future, users will be able to ask Google specific things in conversational terms and find the exact answer they are looking for. As multiple facets of life become connected via artificial intelligence and machine learning, users will be able to ask a Google Home or Amazon Alexa a question and retrieve an entire list of search engine results pertaining to that question.
We are lucky to be able to witness how search engine marketing has changed in the past decade. New technologies and platforms such as Facebook came about and changed how the search engine marketing industry operated. The rise of artificial intelligence signals a convergence of multiple different platforms and complicated algorithms, all in the name of providing users a better search experience. In doing so, more users will launch new searches.
Behind the scenes, however, these powerful machine learning technologies are compiling millions of data points to continue to provide users a better experience but, also, collect an incomprehensible wealth of information that can be used for marketing purposes.
What’s going to separate a successful search engine marketer from the crowd in the machine learning world is going to be the ability to adapt and leverage the advancements in technology to further put his or her sites on the good side of technology.
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Ronald Dod is the Chief Marketing Officer and Co-founder of Visiture, an end-to-end eCommerce marketing agency focused on helping online merchants acquire more customers through the use of search engines, social media platforms, marketplaces, and their online storefronts. His passion is helping leading brands use data to make more effective decisions in order to drive new traffic and conversions.
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