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.

This video course will help you to prepare, and explain a number of concepts:

• AI vs Machine Learning

• How to conduct SEO now that Google is an “AI first” company

• Chatbots

• Programmatic advertising

• Big data

• RankBrain

• Digital assistants

• Data science

• SQL

• Latent Semantic Indexing

• The future of internet marketing

In this course, you will gain a crystal ball with which to gaze into the future of internet marketing, and to ensure that you are ready for all those changes when they come.

Topics covered:

• What Is AI And Machine Learning?

• Google As An AI-First Company

• Preparing For Semantic Search

• Big Data

• Computer Version

• Advertising

• Email Marketing

• Chatbots

• Developing Your AI Skills – Using SQL

• How To Future Proof Your Marketing

- HOW AI IS CHANGING DIGITAL MARKETING -

You may or may not know it yet, but Artificial Intelligence (AI) is rapidly becoming more central to the day-to-day digital world, and the marketing and advertising world is no exception. While the idea of AI may bring to mind bad 60’s sci-fi with futuristic robots, it’s really about so much more than you probably imagine.

AI is a fairly generalized term that encompasses different approaches and technologies that are set up to “think” like humans. It is already incorporated into many technologies you probably already know about, such as voice recognition and chatbots.

Here, we’ll introduce just a few of the fascinating elements and applications of AI technology, and explain why it’s about to become integral to almost every aspect of the digital marketing landscape.

Machine Learning

Machine learning is a type of AI technology exactly what it sounds like: a process in which machines are able to essentially figure out how to problem-solve on their own by drawing on previous data sets, thereby “learning” on their own. In a marketing context, it can be applied to a number of applications - ad targeting, lead generation and search optimization, just to name a few.

To some extent, this part of AI will be the basis on which machines will start making more business decisions, thus, at least theoretically, freeing up time and space for business owners and workers to focus more on the human, creative and financial elements of business and marketing activities. One example of decision-making in this scope is the use of digital personal assistants.

Marketing Increasingly Focused on Consumer Behavior

AI is all about data-driven approaches to marketing and decision making and to this extent is being used to integrate data from different platforms.

Platforms collect and store all kinds of analytics these days as a part of analyzing customer patterns in order to develop automated systems and customer profiles to target certain markets. It looks like, in the near future, computers will be increasingly in charge of bigger and bigger decisions.

They’ll be able to analyze behavior and customer profiles even more closely, thus being able to essentially perform their “own” outreach strategy, building copy that meets the voice of the customers who they are observing online.

In addition, consumers will find themselves (though perhaps unknowingly) handing their purchasing decisions over more frequently to robots who already have a record of their ideas, previous searches, and preferences.

Insight Integration

AI is a useful tool in gathering and integrating data sets from different types of software and other collection tools. The more it becomes developed for this purpose, the more effective it will be in targeting and customizing digital ad campaigns based on the customer avatars and buying journeys.

Thus, via meta-analysis, AI can capture and analyze datasets in much more complex ways than we do now, just using tools directed towards, for instance, single channels (think Facebook Insights), allowing for automation in much more innovative ways than we can probably imagine.

Semantic Searching

Drawing on the ideas of machine learning and meta-analysis as mentioned above, semantic searching refers to the capacity for machines to essentially understand user searches contextually in order to offer a set of results that are customized. AI can do this by understanding more about the contextual meaning of certain search phrases and patterns. It’s also able to understand more detailed and complex relationships between different data sets--so, for instance, incorporating a user’s search history into the results page.

What does this mean for SEO? It means that searching is becoming more nuanced, likely encompassing, for instance, more secondary and long tail keywords as a part of that search. Semantic searching is about getting to the gist of why a person is searching for something, rather than just showing what they are searching for.

Content Creation and Curation

AI can be used for lead generation in the context of content creation, and it’s already been used for several programs such as WordSmith. AI is useful for gathering and reporting on data like sports and market information and finances. On the curation side of things, AI will choose the most relevant content personalized to each unique visitor. A good example of this type of technique is when an e-commerce website shows you the “similar” examples of other products you might like.

A similar thing is employed with subscription setups (like Netflix) where the more often you use the software, the more the AI knows about you and can build on its knowledge to make suggestions. Furthermore, it will also be able to write dynamic emails that are tailored to subscriber’s preferences.

Voice Search and Speech Recognition

AI has the capacity to handle a variety of types of searches, including voice recognition. Moreover, they can integrate various types of searching methods to customize results. And perhaps even more fascinating is the personal assistants we are becoming familiar with today, like Siri, Alexa, and Google Home, are now able to have conversations with each other (or at least will do so in future models). And as far as speech recognition goes, an August 2017 report (viaTechcrunch) claims that Microsoft’s speech recognition system was then at an all-time low error rate of only 5.1%.

Lead Generation

Sort of like an automatic recruiter, AI can actually sift through piles of data to find the ideal customers, clients and even colleagues based on information that it already has and the program that it’s using. Even more fascinating, it can also predict or rate how hot a given lead is. So, for B2B or even recruiting purposes, this can save a lot of time and energy on just basic searching, leaving you more time for things like pitching and sales calls.

There are already several programs available that do this, including Node, which uses metadata to recommend new customers, and even LinkedIn’s Sales Navigator tool which helps users find employment leads.

Chatbots

Chatbots are automated tools that essentially are responsible for interacting with clients and customers. Currently, they are able to do things like answer basic questions and fulfill orders. They are being used by companies of all sizes, and are becoming easier and easier to integrate into websites on a small scale.

It’s worth noting that Facebook will be incorporating chatbots into its messenger app, the idea is that customers can easily message a business page to discuss customer service matters. This is one way in which businesses will likely have easy access to “bots” in the near future. In fact, they have already instigated a program called wit. ai bot for just this reason.

Automation and Personalization

Machine learning and AI is being used as a means of understanding buyer behavior and decision-making and the more it understands; the more advertisers will be able to target their marketing strategies towards consumer preferences.

Currently, a lot of what we’re doing in the digital marketing sphere is “guesswork,” constantly testing and adjusting, experimenting to what we hope to be a more profitable end. While our analytical tools today are far more accurate than they were in the days of traditional advertising, the ability to have “built-in” decision-making tools that essentially learn as they go is really optimal for this type of marketing.

One of the most interesting and possibly useful of all the AI applications in digital marketing is based on the fact that it can use large amounts of data to essentially choose a way that it will direct certain information. This can be applied to ad targeting. To this end, we can expect advances in automation to help with optimization in both B2B and B2C.

Every Digital Marketing Specialist Should Learn about AI

Is AI creepy or cool? Like it or not, it is absolutely becoming more and more ingrained in our world. We may think of it as some sort of invasion of privacy, but part of your job as a digital marketing professional is to understand how to use it ethically. AI-directed tools will become more and more commonplace and are extremely useful tools when it comes to analyzing and predicting the buyer’s journey.

If you’re thinking of going into digital marketing, you don’t have to be a specialist in this field (it’s quite complex and math-based), but you should be able to have a solid grasp of how it will be incorporated into marketing activities to a great extent in the future. Though you don’t need to understand the technical nuances of AI to go into digital marketing, it will be essential to have a keen understanding of how this is shaping today’s digital marketplace on all levels, and how, in turn, it will shape your strategy.

ref: Digital marketing