All of these sci-fi sounding terms are taking over the marketing lingo. Especially machine learning. However, they are extremely vaguely understood as concepts and what are their applications. People speak of great things you can do with them without much content in their sales pitch. You’re somehow always left wanting to know more about how these new technologies are going to benefit you.
Well, you might be staring at your marketing plan wondering what’s going to get you the biggest bang for your buck. Wondering what you could do better, more efficiently and with impact that changes lives?
Before we dive into the topic more there are few things we need to clear to be on the same page. Artificial intelligence covers machine learning and deep learning. They are categories under artificial intelligence hence they build into the whole artificial intelligence.
General AI vs Applied AI
What is the difference?
Many people think that by developing machine learning we are developing artificial intelligence in general except that especially is an applied AI since it is a for a specific function or field. Applied AI is for a very specific field like recognizing website visitor behavior patterns and assigning the right buyer persona to them to tailor the right kind of website content for them. Applied AI is something that is extremely beneficial to marketers, for example in the form of personalizing content to a huge amount of users.
Applied AI is intelligence which is used in a very specific or limited way, for example recognizing user behavior, facial recognition or other such limited field.
General AI is the intelligence in a machine which could perform any task we give them that a human being can also do. General AI is the main goal of AI research and a very common topic in science fiction movies and futurism scenarios.
How are They Being Used Currently?
AI (applied AI) is used in surprising many places currently and you might never be aware of it unless somebody reveals them to you.
- Customer service with chatbots such as IKEA uses in their customer service.
- Amazon Echo – speech recognition for ordering stuff and confirming with a text for example.
- Predictive analytics – such as Emarsys uses to calculate potential revenue on a specific marketing automation workflow you are creating.
- Clickbait production – using machine learning to create clickbait content which we all hate.
Deep learning is a new area of machine learning which is also known as deep structured learning, hierarchical learning or deep machine learning. It is a branch of machine learning which attempts to model high level abstractions in data. Its aim is to move machine learning towards its original goal – Artificial Intelligence.
Parts of deep learning architectures have been applied to fields such as computer vision, speech recognition and language processing.
For marketing and especially SEO this means huge things as searches become more and more personal. In example for predicting ranking optimizing. SEO doesn’t die as a discipline but as every field in marketing it is up for a change.
As most basic machine learning is the use of algorithms to process data, learn from it and make a prediction or decision based on it. There are multiple applications to machine learning and the main reason for its emergence was the thought that what if we don’t need to teach computers to do everything we want them to do but let them learn by recognizing patterns in a data we feed them.
Machine learning – the most talked about thing in the field of marketing and widely in use in marketing clouds which many companies use. For example predictive/dynamic product recommendations in a banner or in an email. Also it can be used to process text to see if a person is complaining or giving a compliment to a brand.
Also more integrated to other services like Spotify which recognizes music styles you listen to and creates a weekly discovery lists for you every Monday (which I always listen through!).
Where It Can Be Used?
As mentioned before we can use machine learning in the following areas already:
- Dynamic product recommendations (eCommerce)
- Predictive analytics if a website visitor will convert or not
- Segmenting customers on a more specific level
- Tailor content to these specific customer segments
- Forecasting customer lifetime value
- Churn predictions of your customer to allow you to focus your marketing efforts to win them back
- Semantic data from social media, reviews and emails to better serve customers
- Creating highly converting display advertisements based on behavior data which is compiled from different parts to optimize on an individual level
All of these are already possible with different software’s. Problem with using them is to align so that they don’t overlap or cause unnecessary expenses. Aligning different marketing technologies to best support your marketing and business objectives can be a challenging and daunting task. Often an external opinion will reinforce your decision and/or uncover things you haven’t thought of.
Understanding the whole field of marketing even on a basic level is becoming increasingly difficult as complexity of the industry rises all the time. Especially now that technology has and will become increasingly integrated into marketing activities.