How Artificial Intelligence is Transforming Modern Marketing

Are you struggling to choose the best marketing strategy or measure the effectiveness and adequacy of your marketing campaign? You are not alone I’m too.

I’m no expert in marketing strategies so to set this straight before you go ahead and read the entire article, but I’m an expert in digital transformation and building intelligent systems that can advance your marketing strategy.

Today, most organizations follow a conventional and traditional approach to develop their marketing strategies. It involves a great deal of effort and requires good study of the market and alignment with the cooperate strategy. However, I would argue that these strategies are predominantly based on past experience and little to do with “your data”. It is rare to see organizations employ advanced analytics to build their strategies. Mostly, due to technical complexities or inability to harvest the data.

Data-Driven Organization

You must have seen this title before. Numerous organizations like to put this title in their strategies to indicate the organization puts data first. Although, this is a great direction to take, however, few organizations do manage to perfectly implement. Only those who really understand how to put “Data-First” manage to succeed in building a data-driven organization.

Building a “Data-Driven Organization” is a rather extremely challenging task. It would take the entire organization to achieve it. Many processes need to be redesigned, rules need to be rewritten and business logic needs to be rethought. Equally, the IT infrastructure needs to be ready to help achieve that from building systems to storing and manipulating data.

Data and Marketing

No matter how good and robust your strategy is, it will be extremely fragile if not based on facts and data. Strategy after all is a process; a thoughtful process; you need to collect data about your organization, products, customers, partners in order to tailor the strategy to work best for you.

The data is available in two places. One within your organization’s systems and the other outside your perimeters. The latter is mostly found in open data. Nowadays, social media and global news on the internet represent a big portion of that data. That is why organizations these days use social media monitoring tools to monitor and observe what people are exchanging about them and their brands.

Social Media and Marketing

Companies today are in a race to attract more customers and promote their products to consumers online and most specifically over social media platforms. It has become a practice to analyze what people say over social media platforms to measure the performance of the marketing and communication department. It’s really such a powerful tool and we have seen the impact they present on the social, economic and political life we have today.

Artificial Intelligence and Marketing

Artificial Intelligence was introduced to solve the inability to process a massive amount of data and spot important things like when people are happy or angry about a service or a product we have. Many tools today offer basic to advanced Natural Language Processing to read the unstructured data make sense of it and present insight that could help organizations improve their services.

AI can be used in various marketing scenarios and I will give a shortlist of potential scenarios where AI can be of help

  • Personalized Recommendations: AI can be used to help deliver personalized content and therefore improve the chance customer click or choose a product or service. With proper data planning, you can collect information about your customer preferences (with consent) and display the relevant products and services.
  • Customer Care: Customer care is a big umbrella that covers interacting with customers, receives feedback and process customers’ requests. AI can be used in various touchpoints within the customer journey.
  • Conversational Agents (Basic & Advanced Chatbots): Chatbots and conversational agents are becoming more and more widely accepted due to the high adoption by many organizations.
  • Content & Website Design: Today, there exist many tools that help in content generation and website designs recommendations. Organizations can easily leverage these tools to easily create and publish compelling contents.
  • Advertisement Bidding: AI is used in all advertisement platforms and organizations can use these available features. For example, you can let google ads decide what best work for you! And without the need to understand how bidding strategies work.
  • Understand Buyer Persona: Understand the buyer persona is key. You can use AI to determine the “intent” of the prospect request and then deliver the request to the right team.
  • Audience Targeting: You can use analytics and advanced analytics to determine the right target audience. You can also use AI tools to screen the public data and generate insights that can help you define your target audience.
  • Topic & Title Generations: Perhaps this is one of the most challenging tasks in AI and today we see quite good advancements in this field. You can generate titles and topics that attract more customers.
  • Customer Churns: Identifying the customers churn is important. You can direct certain marketing campaigns or offer discounts for customers likely to churn.
  • Lead Scoring and Health: You can use AI to assign a scoring for each lead to help sellers quality the lead. This helps optimize the quality of leads and the sales team’s ability to utilize the marketing efforts.

Marketing Recommendation Platform

Realizing the importance of digital marketing and the current gab in finding the right tools to help markers achieve better decisions. We at Noura.AI decided to build a platform that helps people working in marketing and companies make decisions with regard to the services and products they provide.

Musihb (مُسْهِب), is an advanced artificial intelligence global media platform that assists organizations in making decisions in the area of Marketing and Customer Success. Mushib collects Millions of NEWS and SOCIAL MEDIA feeds, analyzes them and provides organizations with the insights and decision choices to help optimize customer experience and improve business outcomes.

Unlike other tools, Musihb provides “recommendations” We call them “Parameterized Recommendations”. Where the AI engine determines the best recommendations and then decide the values within these recommendations that fit your organization. For example, available tools identify your negative sentiment, Musihb’s AI engine on the other hand tells you what improves your sentiment and by which percentage you will probably improve when following the recommendations!

Try it for Free!

We believe AI should be available to all. Most of the available tools are very expensive and few organizations can afford to bear the cost.

We provide a ton of features with very affordable subscriptions fee that is suitable for many. You can also try the tool before you commit to any payment. TRY NOW.

Before you invest in Artificial Intelligence WATCH THIS

Are you thinking to invest in artificial intelligence or get into the data science domain? surely, there has been so much fuzz about it in recent years, big companies and small alike are increasingly investing in these technologies, so the obvious question should you invest now? 

In this article, I’m going to shed light on why should you start to consider investing in AI and how should you approach that. Obviously, this article is not meant for everyone but even if you are not in the IT field this article will highlight why business executives should pay attention to this and how it will help them in their digital transformation journeys. 

Alright, so let me begin by attempting to convince you putting your money, time, and effort into this investment. Let’s look at some numbers here

  • In 2015, a survey by Gartner showed only 10% reported that either they use AI or thinking about using it, while data shows that number has risen dramatically in 2019 to 37%
  • In 2019 the market for Artificial Intelligence was value to about $27B with projected growth to 10X by 2027
  • According to AI contribution to GDP in 2030, by region is expected to be 26% for China GDP 14.5% for north America and 12.5% for my home country

I hope this whet your appetite to know more about the investment in AI. For that I will share with you three things I believe essential for any investment considerations and more specifically so in advanced technologies.

Start a Learning Journey

You need to familiarise yourself with data science and advanced analytics. It’s so easy these day to find good courses online both free or paid. The learning is not just for the purpose of being data scientist but rather gives you understanding of the field you are investing in. Another very important topic you need to research is the problems that you think AI would be of great help. You need to envisage how the use of advanced technologies would really solve a real business problem. In other word, you need to be the digital advisor who uses his/her creativity to solve challenging problems. Remember learning is a journey not a destination. So keep on learning, experimenting and exploring new things 

Work in the Field 

If you can afford to work in a startup or international company do so to gain experience and get exposure to the market and access a large network of customers and therefore explore various challenges.

Surely sometimes, it may not be possible to get a job in this filed, However there are other means such as freelancing and open source communities that you can leverage.

It is very crucial to be equipped with both theoretical knowledge and practical applied experience that teach you what works and what does not.

Due Diligence

This step is perhaps discussed a lot and would vary depending on how you approach the investment. So if you are investing your money in a startup then you would want to look for few things.

  • The robustness of the idea, its viability to market, visibility and impact on business and society.
  • You need to look for the Founders’ past history and current competence and skills because after all they will be leading your investment 
  • Founders readiness of vision, clarity, go to the market and operational plans are very critical. It’s very important that you look for business models that offer resilience and flexibility that can also provide diversity rather than relying on one single product or idea because that could be risky
  • look for a startup that has the right team mixture, it’s like a recipe. Every details matter. Building a thriving culture that value customer empathy and have great values is essential for any business success
  • Check for Market tractions and current customers if any. Validate how will the business model attract new customers and most importantly how fast? Again I stress on the business model and its ability to organically grow in market size and consumption

On the other hand if you are investment your time, skills and energy by beginning a startup you then need to ask yourself five questions: 

  • Am I offering a unique value proposition that solve a problem for a large business segment and there is an urgent need at this time? 
  • Do I have the capability to implement this idea, on time, at budget and offer it on timely manner and acceptable price?
  • Am I building an evolving business model that can sustain changes in market and can easily pivote and tranform to different business models?
  • Am I able to build a thriving culture that attract talents, create shared values and goals? and above all, inspire them to make the impossible?
  • Do I have what it takes to attract customers and investors and be the face and the biggest seller of the company? 

These were the three tips I wanted to share with you today. AI is all about R&D so always look for startups that profoundly exert effort into the research and development because the process always involves trial and error and results only come after many many failed experiments.

Entrepreneurial University: How to Drive Private Sector Innovation?

Think with me! How many great research ideas, papers and projects conducted by university Professors and final year Students are now “on the shelf”? How many wasted business opportunities a company has missed by not having an innovation team or department? But wait, from where great ideas come in the first place? 

As someone who worked many years as a Digital Transformation advisor, I say with certainty, business innovation comes mostly from research. In fact, big companies do have enormous R&D teams and they spend billions of dollars on Research alone. An important question would then be how the private sector and particularly startups can follow the same path?

We at for example, work with university professors on research papers that represent “THE CORE” of our work. We firmly believe our success comes from working on the latest research in Data & AI combined with Business Innovation to create next-level products that can compete with technologically advanced offerings in the market. 

I would guess that you have been intrigued by the “Entrepreneurial University” term in the title. Did I get that right? I have always been captivated by the notion of working with universities to create entrepreneurial thinking, collaborate and solve the knowledge paradox between the academic and private worlds. In fact, this model is widely used by developed countries and considered the second source of funding for academic research in the US.

Finding Common Ground

Finding the common ground between academic researchers and private sectors can be difficult and a road full of hardship and that is mainly in my opinion due to the different mindset between sellers in private companies and scientists. Nevertheless, both parties recognize they need each other to reach their goals. So what is the secret to bridging the gap between the two fields?

The secret in my opinion is innovative thinking. Both fields can embrace innovative thinking and adopt a process. This process should serve as a “connector” between the two fields.

Private Companies Viewpoint

Private companies look for profitability and always measured by their ability to make money. Yes, there are other measures companies employ but at the very end, it is how much money they earned in a given period. So for simplicity, let say private companies do view the world from a money angle. Now, to earn that many companies must implement various strategies to better allocate resources and achieve their goals.

Depending on the company strategy and the type of products they make, traditional products are now very hard to sell. In fact, the consumer has become more sophisticated and demand new experience. Companies have no choice but to transform the way they offer business and continue to innovate. To do that, many have sought to establish an innovation department to start to ideate and bring new ideas.

It is unequivocal that basing products on the latest in research would mostly position the companies’ products as leading in the industry assuming proper marketing and sales strategies. Therefore, working with research institutes would bring great opportunity to business. Realizing its significance, private companies can work with researchers and ensure the result is aligned with the company strategy and the final product is consumer-friendly.

Researchers Viewpoint 

Researchers, on the other hand, focus on the quality of research and the outcomes. Although researchers do consider the practicality of the proposed solutions, however, they don’t tend to focus on the sell-ability of the solutions. It is immensely important that researchers are not distracted by the sales or business matters so they are focus on the quality of outcomes.

Having a clear process will certainly help both researchers and private companies collaborate and produce tangible outcomes without compromise from any party.

Process To Adopt 

Having an independent regulatory body that helps shape the regulation and guidelines that govern the relationship and ensure ethics, seemly and proper conducts are in place is of great value. There is an active relationship today between researchers and the private sector, however, this relationship is not really bound by a clear process that leaves no ambiguity. The below is a proposed process; rather than a simple one; that ensure a consistent relationship:

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Influence & Impartiality

Although the idea of Industry Research Funding seems effectively inciting to the development of research. However, there are growing concerns over the fair-mindedness of research, ethics and impartiality of both research topics and researchers. This has inclined countries to develop guidelines and governance models to help ensure the adherence to properly checked procedures to help avoid conflict of interest and keep preserving the lofty goals of scientific research while enabling the private sector to both contribute to research advancement and help bring innovation to business and consumers.

Financial Model

Perhaps, having a financial model that ensures both researchers and research institutes are compensated well is really needed. The financial model will also incentivize the private sector to invest. The cost of establishing an innovation team at the company would be higher compared to offering to compensate a researcher in a university. Furthermore, the industry research fund will help researchers produce more results. Not only that but also, it enables researchers for example access enterprise-level tools and resources. For instance, researchers can access data annotators in a company or hire someone easily via the company purchase department. They can also build appealing UI that help deliver the solution and show its capability in a better and well-presented UI.

Regulations & Guidelines

The need for setting up regulations and guidelines to govern the relationship between the private sector and the academic research institutes is unequivocally important to ensure the sustainability of the relationship and the yielded outcomes that contribute to business innovation and the increase in the research activities.

The key highlights that need to be taking into consideration whilst planning and building such guidelines and governance models need to ensure:

  • Clear guidelines for Intellectual Property and Patents ownership. This also should include any artefacts such as code and datasets
  • Clear guidelines for future development and usage of the research outcomes. This should also include any packaging and repackaging of any solution.
  • Clear guidelines on the licenses scheme and distribution.
  • Clear guidelines on the compensation scheme and governance model to ensure fairness and avoid any abuse.
  • Clear guidelines on procedures to ensure research fairness as well as correctness and preserve the ethics of research conducts.

It’s also worth it that government need to build a framework that helps both the academic and private sectors collaborate without worrying about complex engagement models and fear of preaching any law. That also should include creating a body that oversights the relationship and ensures adherence to the herewith in framework.

I hope you found this article useful and enriching and would be delighted to receive your kind comments and feedback. Also, please do share your experience if you are an academic and had the chance to work with the private sector., the decision company

I have always been passionate about technology and what it can do to transform business. Similarly, I was very fortunate to have had the chance to work in international company and meet many customers in the middle east. During that time it was obvious to me that the region is in very need for a private AI research company that attempt to bridge the gap between research institutes and private sector. Truth be told, it isn’t simply remarkable to the Arab world but indeed for the whole world. That is why big tech companies are in race to build the most advanced AI platforms.

So, was born to partner with researchers and private sector to build the most comprehensive AI decision support platform. is a young research-led startup company that aims to solve a broad range of decision problems. We believe succeeding in digital transformation requires blurring the divide between academic research and business innovation.

Four research areas. One goal: create an AI decision platform

Supercharging the decisions making backed with four powerful research areas that make you ready for competitively by affect and manage outcomes and reduce risks.

Deep Reinforcement Learning (DRL): Build adoptive models to predict consequences of behavior via interaction with environment. The goal is to create an incentivized agents capable of making and evolving decisions.

Probabilistic Modeling: When making decisions in complex and uncertain environment machine learning algorithms alone are not sufficient. This is where probabilistic models are needed to either forecast or support other machine learning algorithms in making better predictions.

Multi Agent Systems (Game Theory): To reach out to the best possible decision an agent based system compete or collaborate to optimize the recommendation using game theory.

Econometrics: Machine learning algorithms are not built to deal with causalities and causal inference as econometric models do. We combine machine learning algorithms with econometric models to help understand economic and policy uncertainties.

I’m very excited about the challenges and opportunities ahead and I look forward to work with our customers and partners to transform their business.