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During the last thirty days there has been a reinvigorated discussion surrounding artificial intelligence, with “AIO” (Artificial Intelligence Optimization) raising its head on agency websites and blogs.

HTTPS and mobile first seem to be cooling as topics, so attention is turning to Rank Brain.

The reality of this paradigm however is that artificial intelligence optimization is seemingly a paradoxical notion. If we imagine that Google is a child, when the child goes to school and reads a book, we want the child to learn and understand the information in that book. If the book isn’t “optimized” for the child to learn – structured information, images, engaging, positive user experience etc. – then the child won’t learn or understand the content. It is important to design your webpages with this frame of mind.

Optimizing for Rank Brain isn’t something new, or complicated. So why is there this need to turn Rank Brain optimization into a product of its own, when the practices aren’t anything new?

In this article I’m going to explore exactly what Rank Brain is, and isn’t as well as how the pre-existing concepts and practices of good SEO (as outlined by Google’s guidelines) apply to Rank Brain.

What is Rank Brain?

Rank Brain uses a form of machine learning and is used by Google to process unfathomable amounts of qualitative data (written content) into quantitative data (mathematical entities), vectors that the algorithm and other computers can understand.

15% of all queries that Google processes are new, so it’s common for Rank Brain to encounter a query or phrase it hasn’t seen before. Using previously processed data in vectors and shards, Rank Brain looks to make an intelligent guess based on similar queries and meanings.

The number of new queries has reduced by 25% in 2007, but volume has increased exponentially thanks to the rise of smartphones and increased internet penetration rates globally.

Simply put, Rank Brain:
◾Interprets the user query
◾Determines search intent
◾Selects results (items) from the databases

What is machine learning?

Machine learning is a computer science and was defined in 1959 by Arthur Samuel as follows: “Machine learning gives computers the ability to learn without being explicitly programmed”. Samuel conducted the initial research into this field, which evolved from pattern recognition studies and computational learning theory.

Machine learning in essence explores the construction of algorithms and makes predictions based on data and statistical frequencies. Machine learning has been used in a number of software applications prior to Rank Brain, including spam email filtering, network threat and intruder detection and optical character recognition (OCR).

While this is a form of artificial intelligence, it’s not a high functioning form.

Association rule learning

ARL (association rule learning) is a method of machine learning for discovering relationships between variables in large databases using predetermined measures of engagement.

This has previously been used by supermarkets to determine consumer buyer behavior, and is used to produce loyalty coupons and other educated outreach methods. For instance, through store loyalty/points cards, a store can gather data that when analyzed can predict buying patterns and behaviors.

ARL can also be used to predict associations, for example if a user buys cheese slices and onions, it could be assumed they are also going to buy burger meat. Rank Brain uses this principle in providing intelligent search results, especially when a phrase can have multiple meanings.

An example of this is an English slang term “Dench”. If a user searches for Dench it can have three meanings; the slang term, a line of clothing, or the actress Judi Dench. The term can also be associated with individuals, such as professional athlete Emmanuel Frimpong and rapper Lethal Bizzle.

As the query is ambiguous, Google’s own search quality evaluator guidelines explain that the search engine will show as many variations as deemed possible in order to satisfy the users search intent as best they can.

Concepts of association rule learning

The main concepts and rules of ARL are Support, Confidence, Lift and Conviction, but for the purposes of Rank Brain I’m going to focus on Support and Confidence.

Support

Support in ARL is the measure of how frequently the item in question appears in the database. This is not the same as keyword density, or the number of times keyword variants appear.

Confidence

Confidence of ARL is a measure of how often the rule has been found to be true. This is based on associative terms, i.e. if a user searches for “POTUS”, then there is an X% chance that they may also search for, or find, Donald Trump a satisfactory result. They may also find Barack Obama, George Bush or Abraham Lincoln satisfactory results.

Confidence can often be confused with probabilities, as the two principles with regards to organic search are quite similar (if a user searches for X, then Y and Z can also be valid).

Rank Brain uses association rules to satisfy user specified minimum support, and user specified minimum confidence at the same time, and both support and confidence are generally split into two individual processes:

1.Minimum support threshold is established and applied to all frequent items in the database.
2.Minimum confidence constraints are applied to the frequent items, in order to form rules.

Using these rules, Rank Brain helps Google prioritize which ranking signals are most relevant to the user query, and how to weight those signals.

Rank Brain and SEO

Rank Brain was launched in a dozen or so languages (as confirmed by Gary Illyes on Twitter in June 2017) ranging from English to Hindi, and its sole purpose is to help Google provide more accurate results and an overall better search experience for users, satisfying their queries.

The main difference between the pre- and post- Rank Brain world is that before RB, Google’s team of software engineers would amend and alter the mathematical algorithm(s) that determine search results and rankings, and this algorithm would remain constant until an update was made. However, Rank Brain is a part of the core algorithm and is used by Google for all searches (as of 2016), meaning that there is constant change and fluctuation.

This means that search results are now reactive to real world events, as well as a lot more volatile outside of the big algorithm update announcements.

“Optimizing” for Rank Brain

Given how Rank Brain interacts with the core algorithm and other ranking signals, there may be a need to change strategic focus (especially if the strategy is built on backlinks). But Rank Brain is not a “classic algorithm” like Panda and Penguin.

With the classic algorithms, we knew how to avoid Penguin penalties and thanks to guidelines, we know how to satisfy Panda. Rank Brain on the other hand is an interpretation model that can’t be optimized for specifically. There are, however, a number of standard SEO practices that are now more relevant than ever.

The idea of writing content with a “focus keyword” and producing one page for one keyword are outdated. The Hummingbird updated killed this in 2013, and Rank Brain has taken this one step further.

I’ve seen this practice still being used in a number of sectors. When creating content and URL structures, both user experience and keyword matrices should be used, with the focus being on creating high value and resourceful pages.

Different queries = different weighting factors

Because of the way Rank Brain has changed how certain variables and ranking factors are weighted for different queries, it’s no longer a practical approach to take a one size fits all approach with queries (and query categories).

Taking queries that trigger Venice results and the map pack out of the equation, some queries may demand high velocities of fresh content, shorter content, longer content, lots of links… The new weighting model that Rank Brain presents means that there will need to be deviations from the standard best practice.

Internal linking structures

We know from Google’s search quality evaluation guidelines that Google considers main and supplemental content when ranking a page; this extends to pages within a URL subdirectory and pages linked to from the main content.

It’s standard to optimize internal linking structures so that link equity is passed to key pages on the site (as well as deeper pages), but it’s also important to include a good number of internal links to improve the user experience.

What does the future hold?

When Rank Brain was first launched in 2015 it only handled around 15% of queries, but by the same time 2016 Google’s confidence in the algorithm had grown, and it let Rank Brain loose on all queries. This will have been a phased rollout and responsible for a number of changes we saw in 2016.

As Rank Brain learns on the job, it will only get better at understanding semantic and concepts, and relationships between topics and queries. This will benefit voice search results accuracy as well as traditional search results pages and now cards.

In summary

In conclusion, a number of leading figures in the SEO community (including Gary Illyes and Rand Fishkin) have come out in various ways highlighting that Rank Brain isn’t something that can’t be specifically optimized for.

That being said, understanding how the Rank Brain algorithm works is important to understanding the ranking volatility in your (or your client’s) verticals.

Optimizing webpages for the real world is more important in 2017 than at any time in the past. If you want to join a growing group of web designers who have superior search engine skills, it’s time to call ABCO Technology.

ABCO Technology is now located at our new facility at 11222 South La Cienega Blvd. in STE #588.

You can reach our campus by telephone at (310) 216-3067 from 9 AM to 6 PM Monday through Fridays.
Email your questions to info@abcotechnology.edu
Financial aid is available to all qualifying students.
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It is fair to say that public opinion does not favor most online ads — ABCO Technology’s research confirms this fact. In a recent study, we collected open-ended responses to understand what truly makes or breaks an online advertising experience. From the 200 responses we received, comprised mostly of negative comments, combined with our insight from a presentation by Charles Pascal and Bob Castaneda at ABCO Technology’s User Experience class in 2017, we were able to distinguish five user requirements for interacting with advertisements. Complying with these will make your ads less insufferable and may even make customers like you:

•User control
•Instant gratification
•Placement
•Predictability
•Relevance

User Control

One of the 10 usability heuristics is user control and freedom. Not surprisingly, people complained when the ad took control of the interaction and forced them to perform actions (e.g., watch a video ad) against their will. In contrast, they expressed positive affect when the ad was triggered by their action or when they could opt out of the interaction.

For example, if users clicked a button to play a video and the video started with an ad, they were less annoyed than if the video had started playing by itself (autoplay). Additionally, being given the option to skip or dismiss an ad was a valued capability which gave users a sense of control.

Not surprisingly, modal ads, which take away user control and interrupt the current task, while requiring users to interact with the ad — either by dismissing it or selecting a call to action within the ad — were among the most disliked ad types both on mobile and on desktop.

Examples of user comments:
•“The best ads are the ones that you can simply click on to close and you don’t have to view them anymore while you’re on the webpage.”
•“I like ads that do not obstruct content. I can glance to the side and decide if I want to open [them] but am annoyed when I don’t have that choice.”
• “I hate having to close something to seek content on a site. That is nonsense — I didn’t ask for that and I tend to dislike the company even more.”
•“I strongly dislike ads that I’m forced to watch…”

Instant Gratification

Respondents often complained about ads that “delayed” or “blocked” access to content of interest. Examples of such ads include prevideo advertisements, modal ads, or ads that slowed down the loading of the page. The inability to skip an ad hinders access to primary content. The additional load time and delayed content delivery divert attention from the main task, increase cognitive load, increase time between interaction and feedback, and often do not support the user’s primary task.

Examples of user comments:
•“Any ad that makes a page difficult to load or read is annoying.”
•“Video ads before a video you want to watch are okay as long as they are short and give you an option to skip after so many seconds.”
•“I dislike especially the ones that start and don’t let you close for 5 seconds”

Placement

This dimension refers to physical obstruction of content. For example, a modal advertisement with a dark background will obscure the primary content, and a persistent banner will physically occupy many pixels on a page, thus pushing information below the fold — especially on a mobile device. On the other hand, an advertisement in the right rail does not impede the use of primary content, and is in a location conventionally used for advertising. Respondents often complained about ads taking up “space” or “covering” the desired content.

Examples of user comments:
•“I hate disruptive advertising. An online ad should be no more intrusive than a standard magazine ad. That is, it should not flash, demand action, or be placed in the middle of an article. Off to the side or below is best.”
•“I am fond of links to the side and at the end of my pages. I can’t tell you why, but I like them and am much more likely to click on them and check them out than anywhere else.”
•“Since ads are a necessary evil, they must exist. I can tolerate ads that just sit in the right-hand side of the screen, and do nothing. Ads in the middle of Facebook, are okay, unless I see the same one often.”

Predictability

Predictability relates to the user’s ability to anticipate the behavior and response of a site (and is related to the user’s ability to feel in control). Typical unpredictable interactions involve videos that start playing automatically when the user lands on a new page (thus violating the users’ expectations that audio will only play after they have initiated an explicit “play” interaction), or text that gets shuffled around to make room for an ad that was just loaded.

ABCO Technology’s ratings data shows that ads that were highly predictable and easily identified as ads (e.g., appearing in the right rail) were less disliked than others.

Examples of user comments:
•“I hate when ads pop up in the middle of a page while I’m already trying to read, making me lose my place.”
•“Least disruptive are ads that don’t make any noise, or fit well into social media timelines.”
•“Videos that suddenly start as you go to a page can be very disruptive, and shockingly sudden. These also are embarrassing if you are in public and your volume is not turned off.”

Relevance

Relevance refers to whether the ad is related to the user’s goals or interests. Relevant ads are perceived as less annoying than completely unrelated ones. For example, social media advertisements and retargeting ads tend to be based on user’s previous “likes” — which increases the likelihood that the advertisement will be similar to the user’s interests.

This effect is only true to a point. Ads which are “too” relevant tend to become annoying and intrusive, and lose their perceived value. Additionally, if the ads were once relevant, but are no longer relevant (i.e. the item was already purchased or the user did not want the item anymore), the value of the ad (and the brand) was diminished. Those retargeted red shoes that you see on every website quickly become annoying, and after a few weeks turn the corner into being infuriating.

Related links are, by definition, relevant to the user’s current goal. Not surprisingly, they were rated as the least disliked ad type — both on mobile and on desktop.

Examples of user comments:
•“And I know lots of people think it’s frustrating to have ads show up with items you’ve been searching for, but I like it!”
•“… I always find it unnecessary when I see ads for the exact product I was searching, it would be better if they showed similar products instead.”
•“I HATE that something you searched for pops up later on other websites. Way to ruin Christmas!”

Violating User Requirements Increases Annoyance

As discussed above, based on user comments, we identified 5 factors most likely to affect users’ annoyance. The top most disliked advertising techniques in ABCO’s study violate at least 3 out of these 5 factors. For example, modal ads take away control by interrupting the current task and requiring the user to take an explicit action in order to dismiss them. Their placement also covers content of interest and are usually unpredictable.

Autoplay video ads violate user expectations of predictability — audio suddenly starts, startling and embarrassing the user. Often it forces her to fumble around, trying to locate the source of the noise and a way to shut it down. Autoplay video prevents the user from controlling the presentation of content (the ad plays when the system dictates, not when the user does) and delays gratification by adding an extra step: locate the source of the audio, close it, and continue to primary content.

This exercise can be applied more broadly to all of the ad types: the greater the number of expectations violated, the greater the likelihood that the ad will be hated. On that same note, the fewer requirements violated, the greater the likelihood the ad will be tolerated. Thus, if user experience practitioners wish to create engaging ad campaigns, it is in their best interest to meet as many user requirements as possible, while using contextual information to provide meaningful experiences at the right time. Here are some practical guidelines that may help advertising professionals as they design campaigns:

1.Be polite. Ensure your user consents to seeing the information being presented.
Consider your current designs and identify the circumstances in which your advertisements are presented. Do they interrupt your readers mid-sentence? Do they dive into an audible monologue while your customer is busy doing something else? In short — pretend your advertisements are uninvited dinner guests. You wouldn’t want your dinner guests to waltz in, unannounced, and burst into a prepared speech before you know who they are. Let the user decide when to listen. Don’t use autoplay audio or video. Don’t use modals to barge in on the user’s main tasks. Use the periphery to allow your advertisements to make themselves known, so that your users can view and interact with them on their own terms.

2.Don’t delay the delivery of primary content.
Don’t slow your users down. Acknowledge when your users might be more open to novel information; usually this doesn’t happen when they’ve just landed on your site. Wait for them to walk around and find some content of interest, gain their trust, and then present ads. Be nonobtrusive by placing the ad on the right side or bottom of the screen. Better yet, if your advertisement enables them to reach the desired content more quickly, they might be more inclined to view it, but calling an extra step by another name doesn’t change the fact that it is still an extra step to reach content.

3.Prioritize high value interactions when considering advertisement placement.
While your stakeholders might insist that conversions are the highest-priority interactions, it is a designer’s responsibility to balance the priorities of the business with the priorities of the user. If your most valuable screen real estate is being used for intracontent advertising and persuasive modal dialogs, beware of the potential to convey the wrong message: that the company’s revenues are more important than your users’ goals. After all, we cannot even begin to accomplish business goals until we accomplish user goals.

4.Consider platform conventions; limit any unwelcome surprises.
When you are courting a potential customer, you are essentially building a new customer relationship and establishing trust. Unconventional and unpredictable behavior (in a person or in a product) diminishes trust over time. In the effort to be creative, designers sometimes create interactions that are uncomfortable and jarring. In order to win over and put new customers at ease, make it easy for them to locate primary content and your advertisements. If users are confident that they can find what they’re looking for, they will be in a state of cognitive ease, and will be more likely to trust the advertisements you present.

5.Don’t compete with primary content. Enhance it.
Relevance can be hard to achieve, but if your user is looking at a piece of content, it is in your best interest to amplify the current content rather than to compete with it. Using a user’s personal information is potentially beneficial, but runs the risk of being intrusive and even ruins holiday and birthday surprises for some users and their families. Which is more certain: that a user will still need that pot she’s been shopping for this past week? Or that the user may be interested in the article she is reading right now? Use the current content as a basis for how to shape your advertising content.

Conclusion

Even though our discussion in this article has been focused on 3rd party advertising, the guidelines are equally applicable to a site’s own promotions for its own products and services. In fact, self-promotion is even more likely to generate distrust and dislike if done too aggressively, because users have nobody to blame except your company for every single annoyance triggered by your own promos.

For regular advertising, the blame for any user annoyance is shared between the site hosting the ad and the brand being advertised. Both parties have an interest in avoiding antagonizing their customers:
•The website will eventually lose traffic if users decide that the pain and suffering imposed by aggressive ads aren’t worth the trouble. Loyal users are the way to build a durable business on the Internet, but you won’t foster loyalty if ads virtually muscle their way through (or over) content every time a user visits.
•The brand will build a negative reputation if users consistently hate its advertising. Why pay to erode the integrity of a brand and make that brand less valuable?

E-commerce is the future of retail. While bricks and mortar jobs are on the decline, online retail jobs are growing. If you want to join this group of professionals who have skills, which are in demand, it’s time for you to take action and change your working life.
You can accomplish this goal by contacting ABCO Technology. Reaching our campus by telephone is easy. Call us between 9 AM and 6 PM Monday through Friday at: (310) 216-3067. Send your questions to: info@abcotechnology.edu
Financial aid is available to all qualified students.

ABCO Technology has moved. We are located at 11222 South La Cienega BLVD in STE #588. Our mailing zip code is 90304.
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