Marketing Technology Trends for 2019 and 2020: more than 75% of the customers expect that companies understand their needs and expectations

Author: Avi Ben Ezra is the Chief Technology Officer (CTO) and Cofounder of SnatchBot and SnatchApp (Snatch Group Limited)



Marketers, today have a variety of tools available to them to offer consumers and business buyers the convenience, relevance and responsive engagement expected. Customers are now better connected than ever before, they have presence and interact across a broad range of media. The fast pace with which marketing continues to move forward means that it is extremely easy to be left behind and the fast developments in marketing technologies are not something to be ignored. They not only make predictions easier, but also take a load of work off the shoulders of the employees in the marketing department but also increase the way that the rest of the company interacts with its customers, giving them the personalization and interaction that they demand. A good marketing program necessitates that marketers make use of these modern technology trends, since they understand the needs and behaviors of their consumers better than everyone else, presenting initiatives that will continue transforming their company campaigns to meet the challenges ahead.

With the second quarter of 2019 already gone, what are those technology trends that will continue to shape the way marketers plan ahead?

Taking the statistics from previous years and comparing then with the present, there is a significant rise in marketing tech users and companies are seeing noteworthy results in their competitive advantage.

It is estimated that the sharing of marketing metrics with sales teams has grown at a rate of 21% in the last year and a half and similarly, so have the number of data sources used by marketers. The adoption of AI is increasing at an even faster rate and marketers are making more use of coordinated channels too.

What is driving these increases?

The driving force behind the increases is the connected customer, who expects convenience, relevance and responsive engagement. Consumers judge a company from their overall experience and never separate interactions with various departments. Therefore, it is important that they see the company that they buy from as one with the company that they interact with and that they get the same level of service across all departments.

According to consumer surveys, today’s consumers don’t only have high standards for product quality, but also expect these to be matched in other interactions with other services that they might need. Competition is stiff and consumers have all the information about competitors available in seconds through their Smartphones and laptops.

Gone are the days when each department worried about its own performance. Today, sales, customer service, commerce and marketing are responsible for ensuring the entire consumer experience – no department can afford to act independently from the other. The initiatives for these efforts always fall onto the marketing department since it completely understands customer needs and behavior.

How can marketers lead their companies to success?

These three essential technology trends will enable marketers to continue to direct businesses in a successful direction.

  1. Personalization is the top priority

The majority of customers, more than 75%, expect that companies understand their needs and expectations. At the same time, over 50% expect that any offers they receive are personalized. Therefore, personalization is a crucial element and comes with important benefits which include: brand building, lead generation, customer acquisition, up-selling; customer retention and customer advocacy.

Artificial intelligence (AI) is one of the most important marketing tech tools for personalization because it aids marketers to unlock the data needed.

The capabilities of AI are expand continuously and so do the ways that marketers use it: predictive journeys, real-time offers, improved customer segmentation,  personalized channel experience, automated social and messenger app interactions, dynamic landing pages and websites, media buying, and offline/online data experience facilitation and programmatic advertising.

Personalization is important to customers, but so is transparency about how that data is used. Regulators also worry about transparency and recent data breaches have also shaken consumer confidence.

High percentages (over 75%) of customers do trust companies with personal information if it is to be used to fully personalize their experience, but they demand clarity on how the information will be used.

In the past two years, marketers are more mindful about how privacy and personalization can be balanced in order to fully satisfy their customer needs.

  1. Making sense of customer data

Data sources for marketers abound as they go about tallying email open rates, ad click rates and more. This information allows them to engage the right individual with the right information and also at the right time. In 2017, the median data sources available to marketers were 10 and the number has shot up by 15% already in a period of just 2 years.

More data does not necessarily mean a more unified view, as many marketers struggle to make sense of all the data that becomes available. A mix of various solutions is often the preferred set up for some marketers, where they unite data from marketing databases to email service providers. Another one of the fast growing areas in marketing tech is data management platforms (DMP) which are offering a wide range of solutions for this problem and for many others too.

Originally, DMP was mostly used for monitoring ad performance and optimizing media campaigns. Organizations have evolved its uses and now often include the management of customer identity and other solutions. DMP use is on the rise and it will continue to offer marketers a unified solution to their customer identity challenges and vital opportunities in marketing management.

  1. Cross-channel marketing

The top challenge and priority for marketers is to have the ability to engage customers in cross-channel, real-time conversations. This is where proper use of RPA and omni-channel chatbot solutions are valuable.

Even though cross-channel marketing is not a new concept, it is not always easy to achieve since most customers now use an average of 10 channels in their communications with companies. Standard expectations are to have two-way communication with customers. Most messages across channels are duplicates, and there is no coordination between them. The ideal communication, something which most companies are aiming to achieve, is to engage customers dynamically across channels through messages that evolve across each communication channel.

The channels of communication are mostly: website, mobile app, social advertising, video advertising, social publishing, Email, mobile messaging, banner ads, paid search (SEM) and voice activated personal assistants.

Marketers need to start meeting the expectations that customers have for cross-channel engagement. Marketing technology allows for such collaboration between marketing and the other teams within the business, allowing it to be more competitive.

The integration of new technologies will be the way forward for businesses in order for them to be able to better enhance their customer experience. Of the marketing technologies that are expected to have the biggest impact on interactions, data collection, and personalization are the technologies of artificial intelligence and augmented reality.


Marketers need to take time to start understanding how the digital landscape and the technology it offers can improve the performance of their campaigns. Perhaps, starting with the implementation of one trend to may be enough, but incorporating them cohesively can lead to even greater results. Suggested reading: Avi Ben Ezra on Chatbot deployment in Europe. 

L’intelligence artificielle est pour Porsche la technologie la plus importante pour l’avenir … (une brève histoire de l’A.I.)

Les progrès technologiques rapides dans le domaine font de l’intelligence artificielle (IA) l’une des technologies clés du 21e siècle – grâce au Big Data et à l’augmentation exponentielle des capacités informatiques. Le constructeur automobile Porsche mise beaucoup dessus. Mais quel est son fonctionnement?


Il y a 50 ans, L’IA (AI en anglais) était devenue pour la première fois un concept grand public – sous la forme d’un film hollywoodien. En 1968, le réalisateur Stanley Kubrick demanda au supercalculateur HAL 9000 de prendre le contrôle du vaisseau spatial dans son épopée de science-fiction «2001 : l’odyssée de l’espace». Une machine plus intelligente que l’homme?

En tant que discipline universitaire, L’IA avait 12 ans lorsque le film est sorti. En juillet 1956, elle est arrivée au célèbre Dartmouth College, dans le New Hampshire (États-Unis). C’est alors qu’un groupe de mathématiciens et d’ingénieurs électriciens ambitieux s’est réuni au Dartmouth Summer Research Project. Artificial Intelligence est un projet initié par John McCarthy, qui a inventé le LISP – le deuxième langage de programmation le plus ancien au monde.

Naissance de « l’intelligence artificielle »
Après quelques semaines laborieuses cet été-là, les dix penseurs invités avaient produit une plume d’écriture dense et de nombreuses idées. Machines parlantes; des réseaux basés sur le cerveau humain; ordinateurs auto-optimisants; et même la créativité de la machine semblait être à la portée de cette génération fondatrice euphorique. Cependant, leur développement le plus important a été l’expression «intelligence artificielle», qui a donné naissance à une nouvelle discipline qui fascinerait les gens du monde entier à partir de ce moment – et qui a en fait été adopté plus rapidement que prévu.

La même année, Arthur Lee Samuel – l’un des participants à la conférence et un informaticien du Massachusetts Institute of Technology (MIT) – a enseigné à un ordinateur IBM 701 comment jouer aux contrôleurs de jeux de société. Son programme utilisait une méthode permettant à la machine de tirer des leçons de sa propre expérience, en particulier dans les versions ultérieures. En 1961, il a joué contre le champion d’État du Connecticut – et a gagné. Cette approche représentait l’idée de base de l’IA en action : l’apprentissage de logiciels sur la base de grandes quantités de données.

Chant d’ordinateur
Cette année-là également, un ordinateur de type 704 a appris la chanson «Daisy Bell» aux laboratoires Bell et l’a reproduite à l’aide d’une synthèse vocale. Cela a évidemment attiré Stanley Kubrick, car il avait fait chanter la même chanson dans le film du superordinateur HAL 9000. Pour les masses à l’époque, tout cela était de la pure science-fiction; mais aujourd’hui, personne ne tombe de sa chaise avec surprise si leur ordinateur joue de la musique. C’est une autre des capacités de HAL 9000 qui reste encore hors de portée: une IA «forte» ou «générale», c’est-à-dire une IA qui imite complètement ou qui pourrait même remplacer les humains, reste un rêve utopique.

Le test de Turing est appliqué pour déterminer si un développement d’intelligence artificielle est comparable à celui d’un humain. Bien qu’aucun système technique ne réussisse ce test dans un avenir prévisible, les machines peuvent déjà faire mieux que les humains. Par exemple, ils sont extrêmement utiles pour analyser de grandes quantités de texte ou de données et constituent le socle des moteurs de recherche Internet. Intégrées dans d’innombrables applications pour smartphone, nous emportons cette IA «faible» dans nos poches où que nous soyons – et en tant qu’utilisateur, nous en sommes presque à peine conscients. Mais tous ceux qui parlent à Alexa ou à Siri voient également leurs phrases analysées à l’aide d’algorithmes d’intelligence artificielle; John McCarthy a fait un commentaire sans prétention sur le sort des applications d’intelligence artificielle: «Dès que cela fonctionne, personne ne l’appelle plus« intelligence artificielle ».

Deep Blue bat le champion du monde d’échecs Garry Kasparov
L’étonnement est de mise à chaque fois qu’Amnesty International franchissait une nouvelle étape, par exemple en 1997 lorsque Deep Blue battait le champion du monde d’échecs sur ordinateur, Garry Kasparov. Les jeux sont toujours un banc d’essai populaire pour les scientifiques de l’IA, et ils offrent également de bonnes possibilités de publicité.

Jeopardy, par exemple, est un jeu télévisé qui implique que les candidats doivent identifier la bonne question à laquelle un terme donné est la bonne réponse. Les tâches définies étaient généralement formulées de manière à être délibérément ambiguë et à exiger la mise en relation de plusieurs faits pour trouver la bonne réponse – rendant le défi beaucoup plus difficile. Cependant, le système IBM «Watson» a réussi à battre les deux détenteurs de records humains en 2011, après avoir été alimenté avec 100 gigaoctets de texte. Plutôt que de s’appuyer sur un algorithme individuel, Watson en utilisait simultanément des centaines pour trouver une réponse potentiellement correcte via un chemin. Plus nombreux sont les algorithmes qui parviennent indépendamment à la même réponse, plus grande est la probabilité que Watson parvienne à la bonne conclusion.

DeepMind bat le champion du monde « Go » Lee Sedol
DeepMind, une start-up londonienne fondée en 2010 et intégrée au groupe Google en 2014, a ensuite suscité un vif enthousiasme. Elle a développé une application d’intelligence artificielle optimisée lors de l’apprentissage de jeux. AlphaGo s’est fixé pour objectif de battre un champion du monde «Go» humain – ce qui était considéré comme une tâche presque insurmontable étant donné l’extrême complexité de ce jeu de stratégie. AlphaGo a atteint son objectif pour la première fois en 2016, en battant le champion du monde en titre Lee Sedol de Corée du Sud : un jalon attendu depuis longtemps. Actuellement, le programme AlphaZero ne se défait que de lui-même, car il renonce aux exemples de jeux humains et n’apprend qu’en jouant seul : les joueurs humains n’ont plus aucune chance de gagner contre AlphaZero.

Cet exploit est rendu possible par les réseaux de neurones artificiels. Les neurones sont des cellules nerveuses qui forment un réseau auquel une tâche individuelle est attribuée, telle que la vision. Un nombre apparemment infini de neurones sont connectés de manière dynamique dans le système nerveux humain. Le cerveau humain apprend en ajustant la densité de ces réseaux sur une base continue. Les chemins fréquemment utilisés deviennent plus forts, tandis que les connexions négligées dépérissent.

Réseau neuronal artificiel
Un réseau de neurones artificiels tente de reproduire cette structure. Les neurones artificiels en réseau prennent en compte les valeurs d’entrée et introduisent ces informations dans des neurones créés dans des couches de niveau inférieur. À la fin de la chaîne, une couche de neurones de sortie fournit une valeur de résultat. La pondération variable des connexions individuelles confère au réseau une propriété particulièrement remarquable : la capacité d’apprentissage. Aujourd’hui, les réseaux sont de plus en plus basés sur ces niveaux; ils sont plus complexes et plus entrelacés, c’est-à-dire plus profonds, grâce à une capacité informatique accrue. Certains réseaux de neurones profonds sont constitués de plus de 100 couches de programme connectées en série.

Cependant, l’IA doit être formée – dans le cadre d’un processus également appelé apprentissage en profondeur. Dans ce processus, les systèmes reçoivent des informations correctives provenant d’une source externe, par exemple un humain ou un autre logiciel. Le système tire ses conclusions des réactions qu’il reçoit – et il apprend.

Tests pratiques prometteurs chez Porsche
Le DSI de Porsche, Mattias Ulbrich, estime que l’intelligence artificielle est la technologie la plus importante pour l’avenir et qu’elle nous aidera à consacrer tout notre temps à ce qui compte vraiment. « L’IA participera à la création de valeur. De la même manière que les robots nous soulagent déjà physiquement aujourd’hui, l’IA nous aidera à réfléchir et à prendre des décisions lors de travaux de routine », explique-t-il. Les départements de développement ont beaucoup de travail à faire avant que nous atteignions ce point. Une considération clé dans ce travail concerne les aspects de la sécurité et de la vie privée.

Tobias Große-Puppendahl et Jan Feiling, du département principal du développement de l’électronique et de l’électronique, ont abordé le sujet chez Porsche. Les développements tels que la personnalisation, l’intelligence des essaims et la protection de la sphère privée nécessitent tous une IA afin de préserver la confidentialité absolue lors de la collecte et de l’échange de données. L’équipe a pour objectif de minimiser l’échange de données en utilisant un «apprentissage fédéré» dans lequel un système d’intelligence artificielle local situé dans la voiture tire les enseignements du comportement de l’utilisateur. Par exemple, si un conducteur dit «J’ai froid», l’IA devrait augmenter le chauffage. Il transmet son succès d’apprentissage – ou pour le dire autrement, son expérience – au cloud et aux instruments globaux d’IA installés dans ce pays, tandis que des données spécifiques telles que des protocoles de langage peuvent rester dans la voiture. En fin de compte, l’intention derrière les données est la clé : chaque utilisateur exprime un souhait à sa manière, mais attend le même résultat. Pensez à rencontrer une personne dont nous ne comprenons pas la langue, mais elle est en mesure de préciser si elle a froid.

Science fiction pure
Bien sûr, HAL 9000 de « 2001 : l’Odyssée de l’espace » de Stanley Kubrick est également capable de le faire. Mais une rébellion d’IA contre les humains est une pure science-fiction – du moins pour le moment – et la téléportation telle que vue dans Star Trek restera probablement pour toujours un rêve utopique. Après tout, une bonne science-fiction ne reflète pas exclusivement une technologie de pointe peu connue du grand public – telle que l’ordinateur chantant – mais explore également les domaines de l’incroyable fantaisie. Le professeur Sebastian Rudolph, spécialiste de l’IA basé à Dresde, estime que les futurs scénarios de rébellion artificielle sont extrêmement farfelus compte tenu de l’état actuel de la technologie. Il dit que, comme c’est le cas pour toutes les technologies, l’IA pourrait être mal utilisée – et en fait que des erreurs pourraient être commises lors de sa mise en œuvre.

Nous ne devrions donc peut-être pas avoir plus ni moins peur de ce type de développement que du progrès technique en général. Et vu de cette façon, il est logique que nous participions tous à la conception de ce progrès technique. C’est ce que Tobias Große-Puppendahl et Jan Feiling ont intériorisé chez Porsche – et en fait dans le meilleur de la tradition de la société, à la suite de Ferry Porsche lui-même : « Nous ne pouvions pas trouver d’IA qui nous séduisait. Alors nous l’avons construit nous-mêmes. »

The Current Applications Of Artificial Intelligence In Mobile Advertising (source: Forbes)

The concept of self-programming computers was closer to science fiction than reality just ten years ago. Today, we feel comfortable conversing with smart personal assistant like Siri and keep wondering just how Spotify guessed what we like.

It’s not just the mobile apps that are becoming more “intelligent”. Advertising encouraging us to interact and install those apps has made its way onto a way new quality level as well. Thanks to advances in machine learning (ML), the baseline technology for AI, mobile advertising industry is now undergoing significant transformation.


AI can reduce mobile advertising fraud

In 2018, mobile ad fraud rates have doubled compared to the previous year. To tap into the expanding marketer’s ad budgets, hackers have created a host of new tricks to their playbook. According to Adjust data, the following mobile ad threats have prevailed:

SDK spoofing accounts represented 37% of ad fraud. In SDK Spoofing malicious code is injected in one app (the attacked) that simulates ad clicks, installs and other fake engagement and sends faulty signals to an attribution provider on behalf of the “victim” app. Such attacks can make a significant dent in an advertiser’s budget by forcing them to pay for installs that never actually took place.

Click injections accounted for 27% of attacks. Cybercriminals trigger clicks before the app installation is complete and receive credit for those installs as a result. Again, these can drain your ad budgets and dilute your ROI numbers.


Faked installs and click spam accounted for 20% and 16% of fraudulent activities respectively. E-commerce apps have been in the fraud limelight this year, with nearly two-fifths of all app installs being marked as “fake” or “spam”, followed closely by games and travel apps. Forrester further reports that 69% of marketers whose monthly digital advertising budgets run above $1 million admit that at least 20% of those budgets are drained by fraud on the mobile web.

If the issue is so big, why no one’s tackling it? Well, detecting ad fraud is a complex process that requires 24/7 monitoring and analysis of incoming data. And that’s where AI comes to the fore. Intelligent algorithms can operationalize large volumes of data at a pace far more accurate than any human analyst, spot abnormalities and trigger alerts for further investigation. What’s more promising is that with advances in deep learning, the new-gen AI-powered fraud systems will also become capable to self-tune their performance over time, learning to predict, detect and mitigate emerging threats.

AI brings increased efficiency and higher ROI for real-time ad bidding

One of the biggest selling points of “AI revolution” across multiple industries is the promise to automate and eliminate low-value business processes. Mobile advertising is no exception. Juniper Research predicts that by 2021, machine learning algorithms that increase efficiency across real-time bidding networks will drive an additional $42 billion in annual spend.

Again, thanks to robust analytical capabilities ML-algorithms can create the perfect recipe for your ad, displaying it at the right time to the right people. Google has already been experimenting with various optimizations for mobile search ads. The results so far are rather promising. Macy’s, for instance, has been leveraging inventory ads and displaying them to customers’ who recently checked-up on their products and are now in close geo-proximity to the store holding the goods they looked up a few hours ago.

AdTiming has been helping marketers refine their approach to in-app advertising. By leveraging and crunching data from over 1000 marketers, the startup has developed their recipe for best ad placements. “Prescriptive analytics will tell our users when is the best time to run their ads; what messaging to use and how frequently the ad needs to be displayed in order to meet their ROI while maintaining the set budget,” said Leo Yang, CEO of AdTiming.

Just how competitive AI-powered real-time ad bidding can be? A recent experiment conducted by a group of scientists on Taobao – China’s largest e-commerce platform – proves that algorithms are performing way better than humans.

For comparison:

  • Manual bidding campaigns brought in 100% ROI with 99.52% of budget spent.
  • Algorithmic bidding generated 340% ROI with 99.51% of budget spent.

It’s clear who’s the winner here.

AI enables advanced customer segmentation and ad targeting

Algorithms are better suited for detecting patterns than a human eye, especially when sent to deal with large volumes of data. They can effectively group and cluster that data to create rich user profiles for individual customers – based on their past interactions with your brand, their demographic data and online browsing behaviors.

This means that you are no longer targeting a broad demographic of “women (aged 25-35), based in the US”. You become capable to pursue more niche audiences, exhibiting rather specific behaviors e.g. regularly engaging with hair care products in the luxury segment on social media. This insight can be further applied by an AI system when entering an RTB auction to predict when your ad should be displayed in front of the consumer (matching your profile) and when it’s worth a pass.

The best part is that AI-powered advertising is no longer cost-prohibitive for smaller companies. With new solutions entering the market, it would be interesting to observe how the face of mobile advertising will change in 2019 and onward.

IBM Watson Marketing Releases 2019 Marketing Trends Report Focused on Emerging Trends Redefining the Profession in the Shift to AI

IBM Watson Marketing Releases 2019 Marketing Trends Report Focused on Emerging Trends Redefining the Profession in the Shift to AI

Top Drivers Include “the Emotion Economy,” Tech-First Marketers, and AI-Powered Personalization, Supported by New Roles in Data and Agile Marketing

With artificial intelligence (AI) continuing to gain usage among marketing professionals, IBM announced a new report from IBM Watson Marketing identifying a new breed of marketers coming to the forefront. Driven by a growing need to meaningfully structure data to enable actionable, real-time decision making IBM’s 2019 Marketing Trends Report provides practical insights for businesses to stay ahead of the forces driving these changes.

“It’s not often an entire profession experiences a sea change the likes of which has profoundly impacted all facets of marketing, but we are in that moment,” said Sylvia Vaquer, Co-Founder & Chief Creative Officer, SocioFabrica. “This transformation fueled by AI-powered inferential connections provides a rare opportunity to rearchitect the whole ‘marketing house,’ but it’s critical to have practical knowledge to capitalize on it.”

Also Read: HG Data Audience Extends Technographics to Facebook, Adobe, Twitter, and Salesforce Digital Marketing Platfor

The report’s findings provide deep insights into how CMOs and digital agencies are reimagining the marketing function with the following overview giving a blueprint for what it will look like in 2019 and beyond:

  • In The Emotion Economy, Purpose Creates Brand Loyalty – More than ever, consumers are more likely to engage with brands that are authentic, meaning the brand holds strong convictions and delivers on them, versus experience alone.
  • Marketer 4.0: Emergence of the Tech-Savvy “Martecheter” – Until now the greatest advantages for marketers has been in the order of budget, tools and talent. That model is now inverted, driven by the rapid growth of new skills and customer expectations.
  • AI and Machine Learning Make Hyper-Personalization a Reality – The proliferation of data and compartmentalized marketing stacks has squarely put AI and machine learning-based marketing tools at the center of deep personalization. This will change how marketers make decisions and deploy campaigns as AI analyzes and delivers personalized content with massive scale.
  • Director of Marketing Data Becomes the Hottest New Role – Growth of “marketing data” roles will continue as they drive human and technology connections across their organization. This will enable artificial intelligence and machine learning-based marketing tools to analyze data and customer behavior, make recommendations and predictions, and become smarter based on the data fed into them.

Also Read: Okta Names Shellye Archambeau to Board of Directors

  • Agile Marketing Adoption Accelerates, Driving Marketing Outcomes and Culture – Organizations driven by culture change and agile mindsets will widen their lead having a first-mover advantage, especially where AI-powered marketing technology enables the right set of tools to align and measure the proper objectives and metrics.
  • GDPR Actually Helps Marketers Improve Data Hygiene and Customer Trust – Marketers will increasingly focus on improving data hygiene processes, leading to better targeting and higher quality interactions. With similar regulations already in certain parts of the US and the potential for US-wide legislation, marketers will proactively improve privacy, security, and data management as a catalyst for new business models.
  • Digital Marketing Agencies Transform into “Consulgencies” – As the rankings of AdAge’s 10 largest agencies was cracked – for the first time ever – by The Big Four consultancies in 2017, it serves as a bellwether for the industry including smaller and mid-sized agencies: “Consultancy” and “agency” capabilities will converge, driven by a need to build out deep expertise in AI, data integration, customer experience analytics, mobile apps, and custom solution development.
  • MarTech + AdTech = The Holy Grail of Marketing – Although extensively debated, 2019 will at last be the year marketers lean more into the benefits of programmatic ad spending. By achieving better data connectivity between their martech and adtech stacks they will have real-time understanding of customers and ad spend optimization using AI.
  • Customer Centricity Breaks Marketing Silos and Delivers Happiness – Marketing transformations will increasingly focus on creating differentiated customer experiences. This will be supported by more experimentation using contextualized understanding of aggregated customer data across other areas of the organization such as commerce and digital teams.

“As marketers ourselves, we felt it was critical to dissect the industry dynamics that are reshaping the profession to help our peers set a strong vision for their organizations.” said Michael Trapani, Marketing Program Director for IBM Watson marketing. “By understanding the transformative trends – from team structure, to the advent of AI-powered marketing, to heightened expectations for the discipline – the findings in the 2019 Marketing Trends Report will be indispensable for marketers.”

Let's Discover the Marketing Hackathon ! Solvay Executive Master in Digital Marketing… More info ? Join the Info session (16th October 2018 from 6.30 pm Brussels)

“A training programme lasting 17 days, running from November 2018
to May 2019, created entirely for you or for one of your managers”


The programme is aimed at profesionals in communication and markerting who want to go deeper into their knowledge in marketing and/or digital communication.

Key admissions criteria

University degree
At least 3 to 5 years profesional experience
A perfect knowledge of English and French will be required. Courses will be given in either of these two languages.

Exhaustive approach
The Executive Master in Digital Marketing and Communication proposes an in-depth development of your skills in the various areas of marketing and communication.

Adapted to the digital
The Executive Master in Digital Marketing and Communication is a comprehensive course in marketing and communication, totally adapted to the world of digital.

Practical insights
The Executive Master in Digital Marketing and Communication gives immediate answers to your questions, as well as practical exercises and group work tasks during the sessions.


Are you interested in learning more about this 17-day program?
Register for the information session to be held on 16th October 2018 from 6.30 pm at Solvay Brussels School (42 avenue Franklin Roosevelt, 1050 Brussels) – Atrium on the ground floor. Come and listen to the free talk to be given by Hugues Rey about “Revisiting Kotler’s 4 Ps through Artificial Intelligence”. While you’re there, you can ask all of your questions about the Master’s programme. This evening is completely free of charge and includes a stand-up dinner.

IBM Watson Deploys AI for Lego’s Black Friday Interactive Ad: An Analysis