We’re going to be taking a look at a couple of the upcoming digital market trends that are guaranteed to be game changers in 2019

Artificial Intelligence (AI) and Machine Learning (ML). You’re thinking of a distant future aren’t you? Then perhaps you’ll be surprised to know that it’s likely that you’re already using AI and Machine Learning tech on a daily basis; social media platforms, plagiarism checkers, autopilot during commercial flights, Uber… However, we don’t really consider these as uses of AI and ML and take it for granted. AI and ML continue to hustle for their place in the world of digital marketing, and it’s true that most brands and agencies are still relatively reluctant to embrace them. However, the constant evolution of digital marketing means that those who were once hesitant are having second thoughts and investing more in the possibilities of AI and ML. 


But what is ML exactly and how does it differ from AI? Firstly, Machine Learning is the science that goes into making a computer learn and to ensure that it continues learning using new information that has been collected, just like humans. The difference between ML and AI is that the latter requires ML in order to learn yet that it has the capability to take these learnings and act upon them as a human, whereas ML is restricted to a learning pattern.


We are aware that these exist but we are yet to unlock their full potential. Here’s a look at what we can expect in the coming year from AI.




 Our focus today is a step beyond Machine Learning and set our sights on Artificial Intelligence. AI is the name given to a machine that can think or act as a person. For example, when a person asks their AI device a question, the device is programmed in a way so that it can think and respond as if a human being. To determine whether a machine can act as a human being and deemed as AI, the Turing Test must be used. Alan Turing was an English mathematician who found a way to determine whether a computer can resolve tasks in a human manner and fool the interrogator making them think that is indeed a human and not a machine.


Since then, computers have become vital in the way people live their lives but it’s the use of AI that initiated the beginning of a new era. In a world that is governed by the internet and browsing, there is reason to aggregate data about users. Doing so allows companies to invest in technologies that make the interaction between a person and AI flawless, having a huge impact on companies and their operations. In addition to the answering capabilities that a machine has, data learning also gives it the the ability to predict the customers behaviour and reply in a more personable manner. As UX design is key to maintaining an audience on a web page or app, AI does just that. It eases the experience of the user, as it will create instant ‘human’ interactions between the brand and customer, and the capability to keep learning by itself. Therefore, any new data that it obtains means AI can continue to learn and build its knowledge of a user or group of users.


Through this method of learning, AI is able to reach a specific target market, which focuses on age, gender, interests etc. It will find and approach a specific demographic that is desired by the brand. All this help marketers reach their intended audience with their message and interact with them to create awareness by suggesting products or promotional deals.


Plenty of firms already rely on AI, as it can provides ‘human’ connection and deliver a seamless experience between them and the user. Perhaps you’ll be surprised to learn that it’s not only firms that rely on it, but us too. Did you know that on Facebook, when you upload a group photo and it can can recognize your friends, that’s AI. When you want to go from point A to point B and you use a mapping app for route, timing and traffic avoidance recommendations, that’s also AI.


But what does this actually bring to the company investing in it. As Return on Investment (ROI)  is major statistic to determine if whether a company is succeeding or not, it is AI’s ability to retain and help the customer that the company benefits from. As mentioned above, the enhancement of UX with AI facilitates the targeting, delivery of message and shift towards the buying process. It’s the latter goal that the company is ultimately chasing and all the data collected helps companies to create a marketing strategy according to the customers preferences and usage patterns, emphasising their interests to reel them in. Who knows, perhaps you’re the next catch.  


This is just a top line look at what AI has in store for us but we have only just started to unlock its potential. With self-driving cars, in-store customer assistants and super doctors already becoming the norm, who knows what else is just around the corner.  


Alexandros Charalambous

Traine MPC


Affective computing, also known as artificial emotional intelligence or emotion AI, strives for the development of systems and devices that can recognise, interpret, process and simulate human emotion. It other words, the AI see your tears and understands you are sad and reacts appropriately.

Researchers at Dyad X Machina combine affective neuroscience, the psychological study of the neural mechanisms of emotion, with deep learning which is a highly structured version of machine learning and which are inspired by biological nervous systems. Their goal is to create an affective layer which guides us through the world and helps us with our decisions. When we make a decision, we not only look at logical pros and cons of outcomes but we also feel through possible futures to come to a decision. Haohan Wang, a co-founder Dyad X Machina, says their mission is to bring emotion into machine learning.

What is the implication of affective computing for future apps?

This is the trippy part: Future applications will adapt to the user’s emotional state. From the point of view of the app, we as users become a more complex persona that goes beyond our demographic metadata and browser history to the apps working for us. This is attained by using real time physical and magnetic data to determine our emotional state from such inputs as a fitbit which measure HRV (heart rate variability) which, with affective computing, can determine your emotional state. In a sense, it takes us back to the days of biofeedback but in this case the app is reading the data and making the appropriate adjustments and creating an incredibly personalised “in-the-moment” experience based on your emotion. If you are upset you’ll see one version and if relaxed, a different one.

This is one of the most complex projects which applies AI to emotion to date, but there are many companies who have become experts in reading and deciphering emotions externally by reading facial expressions and listening to voice patterns and many you can experiment with your own images.


Google Cloud Vision has a console where you upload a photo and it gives you instant analysis:


 Affectiva Founded in 2009, this Massachusetts startup has raised nearly $20 million from investors that include Kleiner Perkins Caufield & Byers and Horizon Ventures. Affectiva has analyzed nearly 4 million faces across 75 different countries to build an program that in real-time can analyze any of the 10,000 possible facial expressions you could be displaying at any given time. Want to see how it works? They have an online demo you can try which will analyze your facial expressions through your webcam while you watch humorous Doritos adds. Or, you can download their Affdex app on your phone and read your face in real time:

 MIT has created a package of programming tools for building models from behavioral data. These include data from devices as well as speech patterns.

Areas where affective computing is being used include market research, healthcare, automotive and of course media and advertising where MPC is using the technology to create content with the greatest emotional impact.

The motivation for this research is the ability to simulate empathy in machines interacting with humans. The machine should interpret the emotional state of humans and adapt its behavior to them, giving an appropriate response to those emotions.

And all this strives to replicate the interactions humans have between each other. But true benefits come with therapy and education, where the machine’s objective is to improve the human experience and to do that the empathy barrier needs to be broken and what we are seeing here are the first steps.


Dennis Neiman

Innovation MPC