Artificial Intelligence In Digital Marketing

empreender

• Publisher: Bibliomundi
• Year: 2020
• ISBN:
  9781526028617
• Edition: 1
• Topic: Commerce
• Subtopic: Marketing numérique

Being smart in business means knowing what's just around the corner. It means thinking ahead and preparing for inevitable changes that will impact the way business is conducted.This is what allows a business to be resilient and to thrive in a changing environment.Digital marketing is no different.It's affecting the way that SEO works, the tools and software we use, and the way that ads are displayed.As digital marketers, that means thinking about things that could impact on the face of marketing.Artificial Intelligence (AI) and machine learning have the potential to completely change the face of internet marketing, rendering many older strategies obsolete even.

Preparing for Semantic Search

Whether Google Assistant eventually becomes the ubiquitous tool that Google wants it to be or not, the fact remains that Google wants search to become increasingly more natural and human. It already has in many ways.

That means that marketers and website owners need to make some changes to the way they do things. It’s no longer enough to find a keyword and repeat it a whole lot, you now need to work as though you’re speaking with an AI. And that means a couple of things.

LSI: Latent Semantic Indexing

Latent semantic indexing is one of the most important things to consider if you’re interested in improving your SEO and getting to the top of Google. It’s even more critical if you hope to be ready for Google’s AI-driven future. Not only is it a powerful concept in itself, but it is also an important microcosm of the broader changes that we are seeing to SEO today.

Search engine optimization is a big and very important part of digital marketing and if you want to drive the maximum number of people to your website or blog then it's absolutely essential that you have the search engines on board.

In the past, SEO has largely relied on creating tons of content around a certain topic and repeatedly using a set number of keywords or key phrases in that content in order to help Google identify the subject and help the right visitors to find your pages. Unfortunately, a few people began to take advantage of this system and began 'keyword stuffing' by using the same keywords over and over again to the point of distraction. Google had to get smarter and so it did.

Today, using the same keyword too much will get you into trouble. So what does Google do instead? It looks at context and the broader subject of the article. In other words, it looks for synonyms and related terms and this also gives it the ability to better understand what your page is about.

For instance, if you had written an article about “decision trees”, then in the past Google could theoretically have gotten confused and brought your site up as a result when someone searched for trees. It may have thought you were talking about decisions about trees!

Now though, it can look for related terms like “flow chart” and thus help to more accurately match article to reader. LSI actually comes from mathematics, and uses a technique called singular value decomposition. This means that it will scan unstructured data and look for the relationships between the words and concepts within.

How to Handle LSI

So how do you make sure your site is LSI optimized?

Short answer: you don't.

While it is obviously tempting for SEO companies to now start offering their LSI optimized services, the truth is that you should have been doing this all along and without thinking about it. That's what the best web marketers like Andrej Ilisin have always recommended and it’s what Matt Cutts advises as well.

In short, writing naturally should mean that you are including synonyms and related topics. Otherwise your writing is going to sound pretty repetitive. The moral is what it's always been: stop double guessing and just write for the reader! This is something we’ll come back to again and again with regards to preparing for a smarter Google.

But there are also some other tips you can keep in mind if you want to ensure that Google knows what you’re talking about.

First, make sure that you use more than one search phrase. It’s a good idea for a whole host of reasons to use a combination of different search terms, rather than targeting just a single one. Seeing as Google will often show results that don’t use the exact key phrase the person searched for, it makes sense to try and include a few popular iterations of the same term.

Likewise, you should make sure to use good and varied vocabulary around the topic. This helps to better demonstrate the context and the subject matter of your article. Rather than filling an article with random synonyms, think instead about words that would often occur alongside the topic you’re working with (such as our earlier example of flow charts.) This is called co-occurrence, and it’s the kind of thing that machine learning algorithms love!

Structured Data

The other big concept that SEOs need to consider in order to be ready for the AI Google of the future, is schema markups, also known as structured data, also known as rich data.

Remember: Google’s aim is to enable Assistant to answer natural language question with useful responses, which will draw on information found on the web. Google doesn’t just want to pull up a list of useful search results, it wants to be able to answer questions. So if someone asks how to make bolognaise, it will simply read out the ingredients.

In order to do this, Google needs to be able to find that most relevant information in a passage of text, and then pull out the specific answer. This takes the concept of RankBrain to the next level, allowing it to understand not just what an article is about, but how each paragraph in that article functions. The problem is that Google’s AI can’t quite do this yet. At least not well enough to be able to usefully provide answers for people without occasionally including completely nonsense!

That’s where schema markups come in.

The idea of a schema markup, is to essentially annotate your articles and blog posts by telling Google what each bit is and what it does. Essentially, you are saying “this is a list of ingredients” or “this is a user rating.”

This also helps Google to provide what are known as “rich snippets.” Rich snippets are search results on the SERPs (Search Engine Results Pages) that include more than just a meta description. You might see a search result listed for instance that also includes bullet point steps, or that includes ingredients for the meal. This way, the user can see the information they’re looking for without even needing to leave that website!

How to Use Markups

Markups look a lot like HTML. Here’s an example of what this might look like:

 

<DIV ITEMSCOPE ITEMTYPE=”HTTP://SCHEMA.ORG/LOCALBUSINESS”>

<A ITEMPROP=”URL” HREF=”HTTP://WWW.FIREFLY-FLORAL.COM”><DIV

ITEMPROP=”NAME”><STRONG>THE CANDLE FACTORY</STRONG></DIV>

</A>

<DIV ITEMSCOPE ITEMTYPE=”HTTP://SCHEMA.ORG/ORGANIZATION”>

<SPAN ITEMPROP=”TELEPHONE”>888-888-8888</SPAN>

</DIV>

 

That is basically telling Google that you are talking about a local business (The Candle Factory). You can also use schema to highlight product names, authors, aggregate ratings, software applications, restaurants, movies, and much more!

To use this yourself, you can either look up the HTML code and implement it yourself, or you can use Google’s handy markup helper: https://www.google.com/webmasters/markup-helper/u/0/.

Here, you will simply share the URL of the page you want to markup, and it will then provide you with the opportunity to create the necessary tags.

There are also plugins you can use to the same end through WordPress.

The Good and the Bad of Schema Markups

The savvy among you may have noticed some worrying issues with schema markups. Specifically:

they encourage people not to visit your website!

Let’s say you have a recipes website, and you included an article on cooking bolognaise. You probably did this, hoping that people would search for it on Google, find your website, and then visit your page in order to read about it. In doing so, they might also click on a few ads, they might buy an affiliate product, or they might just remember your brand so that they come back again in future.

But if Google simply takes your key information and shares it, then there is no real incentive for them to actually visit your webpage. As such, there is no chance they will click on your ads, or buy your products. They’ll not even know that the information came from your website!

Essentially, Google is this way using our intellectual property without any remuneration – which has upset a lot of webmasters, businesses, and marketers.

So should you avoid using these features altogether? Unfortunately, that is not really an option. Remember, Google also uses markups in order to provide rich snippets. These are the more media-rich search listings which include things like star ratings, images, bullet points, and more. These really help a webpage to stand out in the SERPs, and thereby ensure that more people click on that listing. And while you might not get any benefit from having Google read your ingredients out, if you don’t include markup language, then it will just get that same information from one of your competitors. Google wants us to use schema markups, and that means that it will likely reward those sites that do with a little SEO boost. For all those reasons, it’s essential that you keep using this strategy even though you might be giving Google free information in doing so.

In future, if more and more people talk to their Google Assistant rather than browsing the web for information, then there’s a chance Google might need to rethink its policy: lest it face a rather big backlash from content creators!

 

Big Data

You might hear the term “big data” thrown around a lot and not fully understand what is meant by it. In this chapter, you will be enlightened and learn how big data can help you and your business, both now and in the future.

Essentially, big data is nothing more than large data sets. These large data sets are increasingly common online, seeing as everything online is easy to measure and document. If you think about a company like Google, it has immense data sets that it works from, describing the search history of billions of users.

But even a standard website that gets 1,000 visitors a day will work with huge amounts of information. A website will naturally record each of those visits and will also store data about each one – such as the country of origin, and the length of time spent on the site. In a few weeks, this data will likely crash a lot of spreadsheet software!

The reason that big data is featured in so many discussions is that it is very difficult to handle. Making sense of such huge amounts of information requires a lot of smart math, while simply storing and handling that kind of data requires a lot of storage and computational power.

But the potential value of big data is also absolutely huge. Big data provides patterns and insights that you simply can’t get by observing a few users. This is essentially how machine learning works – by looking for patterns in massive data sets. The difference is that this is being leveraged in a slightly different way.

Predictive Modelling

Predictive modelling is a process that involves data mining and probability to forecast potential future outcomes. A model is created using a number of “predictors.” Predictors are variables that are thought to influence future results.

Once data is collected for those predictors, a statistical model can be created. That might use a simple linear equation, or it might use complex neural networks. Either way, statistical analysis can then be used in order to make predictions about how things are likely to go in future.

With regards to marketing, this can help provide better customer insights, better lead scoring, campaign nurturing, upselling and cross-selling, personalized product recommendations and more! Amazon is an example of a site that uses big data in order to provide personalized product recommendations. Amazon doesn’t just use a database of items grouped together (which would be almost impossible to maintain) but rather generates data automatically from every single transaction and sale, and then looks for patterns. It will see what products tend to be bought together (there’s that co-occurrence again) and can therefore use this information to show items that it thinks a user might want to buy next!

Likewise, when it comes to lead scoring, big data can be immensely useful. Lead scoring means understanding which leads are likely ready to purchase and which are not. This is immensely useful information for companies that might want to send sales letters to the cross section of their mailing list that they think will actually buy from them (rather than being put off by the amount of sales material they’re receiving).

Amnesty International uses segmentation and “predictive modelling” techniques in order to better identify the right groups to market toward. By collecting data and then looking at what that data reveals about the kinds of people who donate, Amnesty International knows who it should be targeting with its ads, how much they are likely to spend, and how they’re likely to do it. Any charity can benefit from this kind of data analysis, as can any business.

Collecting Big Data

If you want to start collecting data for your business, there are a wide number of plugins and tools you can use to do so. You should find that a lot of tools, such as Google Analytics, will allow you to export massive amounts of data in order to work on.

You can then choose to use this information yourself, or to outsource it to a data science organization that can use that information to provide valuable, useful insights.

Another good idea to prepare yourself for the future, is to allow users to create profiles. By doing this, you can collect much more data on individual users, and in future provide better recommendations on an individual basis too. This is something that stores have been doing for decades with loyalty cards, but of course the digital nature of selling online creates even more potential opportunities!

 

Computer Vision

As mentioned, computer vision is the ability for machines and computers to “see” by learning from huge data sets and machine learning. By observing countless images, a machine can learn to identify images in an object, or to navigate an environment without crashing into things. What does this mean for the future of SEO?

One BIG thing – and one thing that you should make sure that you are ready for – is that Google will likely start paying more attention to images on websites.

Traditionally, we have been told to avoid using images for things like site names. Why? Because Google can’t “read” and image, and therefore we won’t get any SEO benefit from that.

But Google does have software that can read te...