What is the “Robotisation of Marketing”?

Home What is the “Robotisation of Marketing”?

By: Dr. Mohamed Abdulzaher – (Globalization 4.0: The Future of Media in the Age of 7G Journalism, Intelligence-Integrated Public Relations Model, Artificial Intelligence Journalism for Research and Forecasting (AIJRF), (July, 2021), UAE.

In 2019, I began to speak, write, and coined the concept of Robotisation of Marketing (RM).  Specifically, how the nascent technologies of the Fourth Industrial Revolution (4IR) could impact the restructure of marketing concept, and total change from the digital marketing to a new phase of AI and robotisation of marketing, as it is a part of the Artificial Intelligence Journalism era.

As I talked before, the new concept in the marketing world, which is “Robotisation of Marketing” will replace the “digital marketing” concept as a result of Artificial Intelligence (AI) journalism and the Fourth Industrial Revolution. The future of Robotisation of Marketing depends on how we will use the new techniques of Artificial Intelligence journalism. Robotisation of Marketing will make a great change in reaching the public; soon we will find giant marketing companies that rely on AI, Robot, 3D Printing, and Big Data marketing analysis.

Robotisation of Marketing Growth: Going up with the growth of the Artificial Intelligence journalism, there is a significant growth in Robotisation of Marketing over the next decade. This is what we can see as a great growth in measuring customer opinions, and reaching the target customer, and replacing human element in digital marketing.

It seems AI is making headlines across every industry these days, and marketing is no exception. In fact, AI is the leading technology where marketers expect the most growth over the next two years. Marketers anticipate AI use will grow by 53% — a much higher rate than any other tech types. As the new kid on the block, AI is attracting attention for its emerging and future marketing use cases, according to the Fourth Annual State of Marketing, which had a survey in about 3,500 global marketing leaders.

About half (51%) of marketing leaders are already using AI, with more than a quarter planning to pilot it in the next two years. Unsurprisingly, high performers lead the way with 72% reporting current use. While usage seems high for a tech type that’s still in its infancy, AI has its roots in tactics like product recommendations and predictive lead scoring, which successful marketers have been using for years. 57%of marketers using AI say it’s absolutely or very essential in helping their company create 1-to-1 marketing across every touchpoint.

Robotisation of Marketing Tools and Solutions and a New Revolution in the Future of Marketing.

Robotisation of Marketing (RM) is relying on two different types of technologies:  digital technologies and physical technologies, and both have many tools and solutions that can play a significant role in the Robotisation of Marketing era. We will present the most used tools, and their importance in enhancing RM functions.

Digital TechnologiesPhysical Technologies
Big DataAugmented and Mixed Realities 
Machine LearningAdvanced human-machine
Cloud computing Robots 
Natural-Language Generation (NLG) 

Robotisation of Marketing Digital Technologies

  1. Big Data: Robotisation of Marketing Main Engine

​Big Data analytics is one of the most important tools for Robotisation of Marketing era, it is the main and effective engine, that plays significant roles in all stages of Robotisation of Marketing. 

Starts with: Initial planning for marketing campaigns, then gathering information about the target audience, determining the general objectives of the marketing plans, selecting the media  and outreach tools, achievement of the goals, and the final evaluation, even in the stage of analysing reactions of a marketing campaign, Big Data is moving everything.

Big Data is providing insights into which content is the most effective at each stage of a sales cycle, how Investments in Customer Relationship Management (CRM) systems can be improved, in addition to strategies for increasing conversion rates, prospect engagement, conversion rates, revenue and customer lifetime value. For cloud-based enterprise software companies, big data provides insights into how to lower the Customer Acquisition Cost (CAC), Customer Lifetime Value (CLTV), and manage many other customer driven metrics essential to running a cloud-based business.

Here are more ways the marketing industry is impacted by big data:

  • Big Data enables targeted advertising

Publishers are gaining more data on their visitors and this allows them to provide more relevant advertising. Google and Facebook are already doing it with their amazing targeting options but third party vendors will soon have the same array of choices.

  • Big Data makes it easy to create more relevant content

Just like advertisers are able to offer more relevant advertising, marketers, bloggers, and website owners would be able to share content that’s more personalized to their customers. 

  • Big Data allows marketers to adjust prices in real time

“Pricing has always been one of the main priorities of marketers for monitoring and adjusting. But with the big data, marketers are now able to adjust their prices in real time.

  • Big Data improves customer loyalty

“Through more targeted advertisements, more relevant content and personalization, customers are more loyal to brands than ever. And this is only increasing with time. 

  • Big Data makes it simple to measure ROI

It’s surprising how many companies or marketers have no idea how to measure ROI. But big data takes all channels and activities into account, providing a cost-benefit analysis for each of those elements.

  • Big Data provides more accurate testing results

“Because big data involves huge amounts of information, it enables companies to analyse and test more than just variants of a single factor but rather conduct testing of all sorts of additional data including prior visitor histories.

  •  Big Data allows for semantic search

A semantic search is a type of search which is able to recognize natural speech patterns and provide relevant results based on those inquiries. Incorporating this into your website and make your search functionality and the user experience.

Big Data analytics helps to increasing sales for commodities and services compared to the Traditional or Digital marketing in terms of::

  1. Differentiating pricing strategies at the customer-product level and optimizing pricing using big data are becoming more achievable. 
  2. Big data is revolutionizing how companies attain greater customer responsiveness and gain greater customer insights. 
  3. Customer Analytics (48%), Operational Analytics (21%), Fraud and Compliance (12%) New Product & Service Innovation (10%) and Enterprise Data Warehouse Optimization (10%) are among the most popular big data use cases in sales and marketing.
  4. Supported by Big Data and its affiliated technologies, it’s now possible to embed intelligence into contextual marketing. 
  5. Forrester found that big data analytics increases marketers’ ability to get beyond campaign execution and focus on how to make customer relationships more successful. 
  6. Optimizing selling strategies and go-to-market plans using geoanalytics are starting to happen in the biopharma industry.
  7.   58%of Chief Marketing Officers (CMOs) say search engine optimization (SEO) and marketing, email marketing, and mobile is where big data is having the largest impact on their marketing programs today. 
  8. Market leaders in ten industries Forbes Insights tracked in a recent survey are gaining greater customer engagement and customer loyalty through the use of advanced analytics and Big Data. 
  9. Big Data is enabling enterprises to gain greater insights and actionable intelligence into each of the key drivers of their business. 
  10. Customer Value Analytics (CVA) based on Big Data is making it possible for leading marketers to deliver consistent omnichannel customer experiences across all channels.
  1. Machine Learning: The Best Way to Optimize Robotisation of Marketing Campaigns

Machine Learning Apps have the capability to provide new marketing solutions to understand, anticipate and act on the problems, and trying to solve faster and with more clarity than any competitor.

Machine learning is taking contextual content,  marketing automation including cross-channel marketing campaigns and lead scoring, personalization, and sales forecasting to a new level of accuracy and speed. Measuring marketing’s many contributions to revenue growth is becoming more accurate and real-time thanks to analytics and machine learning. Knowing what’s driving more Marketing Qualified Leads (MQLs), Sales Qualified Leads (SQL), how best to optimize marketing campaigns, and improving the precision and profitability of pricing are just a few of the many areas machine learning is revolutionizing marketing.

Six Ways Machine Learning Can Enhance Robotisation of Marketing

  1. Boost Customer Experience

Machine learning can help marketers leverage real-time data in cloud computing environments and engaging it with the customer’s demands, and it also helps to enhance the Brand awareness.

Machine learning can improve the customer’s online shopping experience in many ways. An example is Kate Somerville, who has combined a Magento e-commerce platform with nChannel to great effect. They used machine learning to create a more personalized shopping experience by responding to real-time data. This has boosted traffic, conversions and of course, revenue.

  1. Guarantee a Better Personalization

Machine Learning is providing  marketers a big chance to deliver a superior customer experience at scale, Machine Learning Has the ability  to suggest content, or ADs that the audiences  are  most likely to enjoy, based on everything they previously looked for, watched, ignored and rated. 

For instance, Netflix, which has over 100 million members in 190 countries and thus has had to go “beyond rating prediction and into personalized ranking, page generation, search, image selection, messaging and much more.”

A study conducted by Boston Consulting Group revealed that personalization efforts can boost revenues by 6% to 10%, and is expected to shift $800 billion in revenue towards the 15% of companies able to successfully implement it over the next five years. The benefits for brands who implement personalized marketing effectively extends to their customers as well. Customers favour personalized marketing, as evident by 42% higher conversion rates from personalized calls to action (CTAs), 40% higher average order values, and 600% higher overall conversion rates.

  1. Deliver Well and Consistently Customer Service

Machine Learning and AI tools can provide companies by the fastest and perfect customer service in 24X7 availability, thus, ML-powered chatbots not only help digital marketers save money, but also ensure better business outcomes.

For an Example, The eBay chatbot built for Google Assistant, which is “the most advanced e-commerce chatbot out there. And is also the most used,” helps customers find the best deal on their preferred products by using voice search, and pasted image. 

Artificial Intelligence (AI) enabled chatbots are taking Customer Relationship Management (CRM) to a new level as business-to-business, business-to-consumer, and consumer-to-business communications is both automated and improved by way of push and pull of the right information at the right time. Chatbots also provide benefits to customers as both existing clients and prospects enjoy the freedom to interact on their own terms. Our research indicates that over 50% of customer queries may be managed today via AI-based chatbots. Next generation chatbots will leverage hybrid voice and text solutions to provide an increasingly seamless and human-like communications experience. Conversational AI is continuing to evolve, eventually anticipated to provide a near perfect replacement for human CRM interaction, with fewer errors, and improved opportunities for product and service upsell to consumers as well as greater overall satisfaction.

  1. Automate Content for Marketing and Customer’s Streamline Risk Prediction

Machine Learning can help marketers to generate valuable advertising content automatically.

Machine Learning helps in analysing and significantly reduce customer churn using machine learning to streamline risk prediction and intervention models.  Artificial intelligence tools and Machine Learning can focus on analysing problems and processes and finding a way to optimize them.

Google is transforming its content ranking strategy. The machine learning algorithms used by Google automatically mine through the data and identify the best and original content on the specific topic and rank it accordingly. The creation of the actionable content that targets the right audience is fundamental to the success of businesses. Companies are leveraging AI to produce the content automatically, communicate with customers using chatbots, and create personalized content for the customers. Marketers can also gain valuable insights into the top-ranking content by using AI tools. Moreover, they can get recommendations on how to enhance their existing content and which channel will lead to more sales.

  1. Improve Further Customer and Prospect Databases 

Machine Learning technologies can create massive automated databases for all existing customers, their most important preferences, and personalize the content targeted to them. Consequently, updating the databases of commodities and services, or marketing plans, according to the preferences and nature of the target audience.

Using machine learning can help to qualify the further customer and prospect lists using relevant data from the web, predictive models including machine learning can better predict ideal customer profiles. Each sales lead’s predictive score becomes a better predictor of potential new sales, helping sales prioritize time, sales efforts and selling strategies. 

  1. Plan and Manage Successful Marketing Campaigns Automatically

Machine Learning tools would be very beneficial in devising efficacious marketing strategies, based on the ability in forecasting customer behaviour, and a huge database.

With the help of Machine Learning tools, companies and marketers can determine which mode of marketing received more engagement from the customers. Based on this, marketers can choose that medium for future advertisements to generate more sales.

  1. Cloud Computing: The Oxygen for Robotisation of Marketing Data

Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared poll of configurable computing resources (e.g. networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management efforts or service provider interaction. This cloud model is composed of five essential characteristics: on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service.

Cloud Computing can help Marketers in:

  • Getting the access to reach a large audience, engage with a wide range of prospects, and grow marketing resources at the same pace as your business.
  • Controlling unique marketing content and deploying strategies without a limited number of people.
  •  Helping marketers to access their files with ease, improving collaboration and communication both within the team and with clients in a very confidentiality way.
  •  Reducing the cost and fees, where the Cloud Computing can companies to cut cost in expensive infrastructure, or spending money on costly hardware or licensing fees.
  • Testing marketing strategy and tracking the prospects and customers through widely available and extremely affordable Customer Relationship Management applications.
  1. Blockchain: The Trust for Secure for Robotisation of Marketing Processes

Blockchain is a huge system which helps in recording data and information, in such a way that it makes it difficult for anyone to hack or cheat the system. The technology makes the system transparent and unchangeable.

Blockchain will provide marketers a big chance to connect better with customers, and enhance the existing lists of companies, customers and strategic marketing campaigns, ​Blockchain can enhance Robotisation of Marketing campaigns in different ways, one of them is  the ability of Blockchain to detect customer’s fake accounts, which  helps companies save money that is usually spent on advertising to fake profiles and is never accounted for. ​

​Blockchain has to offer to the marketing industry:

  • A Way to Share Rewards

The distributed ledger technology automates payments at any scale, making it possible for brands to send micro-amounts to the consumers. 

  • Optimized Advertising Value Chain

One of the focus areas for blockchain in marketing is Adtech. Managing digital ads is a prime candidate for moving to a secure, transparent, and accountable distributed ledger.

  • Verifies Data for Customer Intelligence

Blockchain can gather, check, store, and automatically update databases with a little human intervention. 

  • Targeted Content Delivery

Data can be linked to a hyper-personalized segment of insight generation rather than using automated insight generation for effective targeting.

  • Serverless Architecture

Serverless architecture is a better option than traditional cloud hosting to deal with thousands of transactions as it allows stores to scale as per customer demand. 

  • Transparency and Trust

Blockchain helps advertisers select the right publishers, quantify the results of an advertising campaign, helps build trust, and prevent fraud.

Blockchain will enhance the Robotisation of Marketing by removing companies’ abilities to automatically collect data from customers without also offering to reimburse them for its value.
For an Example, the Brave browser is changing the way that users interact with online advertising. Rather than pushing online ads, Brave users opt-into viewing ads and receive Basic Attention Tokens (BATs) for the ads with which they interact. It’s a completely new way of viewing advertising, by trading the value of online attention, rather than simply the trading of space for potential ad sales.

  1. Natural Language Generation (NLG): The Marketing Data-driven Wave

Natural Language Generation is beneficial for content creation, which could help to create an infinite amount of vacuous content and media reports, with little purpose other than to generate traffic and marketing and advertising revenues. 

NLG is a subset of NLP that gives computers the ability to understand the meaning and context of text or speech inputs. Whereas NLP focuses on turning human language inputs into data that machines can work with, NLU offers more profound comprehension of what the inputs mean. This ability is a crucial factor in creating technologies that allow users to interact directly with computers in a meaningful way.

Natural Language Generation (NLG) The ability to generate human language output from data inputs. NLG allows computers to communicate data in a way that humans can understand by creating language outputs, as the name describes. NLG is the side of chatbots that allows them to respond to messages naturally, or the ability for Siri to respond to you coherently.

Natural language generation technologies organize data analytics and content creation by turning statistics and facts into automated yet quality articles.

Automatically generated content is created automatically by AI bots. That is done with little or no human involvement in the process. AI tools that are generating automated content use natural language generation (NLG) technologies. Their main goal is to convert your data into accurate, legible, and well-written content formats.

Namely, natural language generation is a form of artificial intelligence that generates natural language from your structured data. When implemented strategically, an NLG system can transform numbers in your spreadsheets into engaging and data-driven narratives. Some of the tools we will mention later even use associations between words to improve the writing process.

Robotisation of Marketing Physical Technologies

Physical technology is technology that is tangible such that it physically exists. This is a relatively new term that is used to differentiate between technologies that are mostly intangible code and data from those that are mostly physical.  Information technology is an industry centred around software that has become so lar mostly physical. ge that it is practically synonymous with technology. In this context, the term physical technology can be used to denote technologies that are more than software.

When we talk about Physical technology in the Robotisation of Marketing, here I mean all the technologies that can be touched as hardware, or any kind of tools and solutions that can transfer content, whether broadcast, publishing, and posting such as:  virtual, augmented and mixed reality tools, robots, 3D printing, which helps in designing different forms and advertising and marketing materials.

There are many such tools that we can we call: Robotisation of Marketing  Physical technology, and here we will show only three examples, as follows:

  • Digital Reality Tools and Solutions
  • Robots
  • 3D Printing 
  1. Digital Reality Tools and Solutions: The Core Dynamic Tools to Accelerate Robotisation of Marketing

Digital reality refers to the wide spectrum of technologies and affordances that include Augmented Reality, Virtual Reality and Mixed Reality, 360° video, and the immersive experience, enabling simulation of reality in various ways.

Imagine you are a service technician. During a maintenance task, you use your iPad to scan an AR code from a machine and see 3D-animated KPIs through your display. Next, you use your MR glasses to repair a complex item of machinery by video-calling an expert, who sends animated 3D hologram instructions on to your device. The next day, using a headset, you undergo mandatory safety training and certification in a VR simulation.

Digital reality can play significant roles in collecting, classifying and distributing data, it would thus become increasingly essential for processing the companies’ big data, as well as dealing with the variability of the task,  where it can also offer various prospects for enterprises to transform areas such as internal workforce communication and collaboration, workforce training and simulation, and customer service, plus managing the marketing and advertising maps. 

This Concept was coined by Dr. Mohamed Abdulzaher, it has been presented on his researches and his book: Artificial Intelligence Journalism: the 4IR and Media Restructuring, Artificial Intelligence Journalism for Research and Forecasting (AIJRF) 2019.

All the previous content been quoted from:

  • Mohamed Abdulzaher, Globalization 4.0: The Future of Media in the Age of 7G Journalism, Intelligence-Integrated Public Relations Model, Artificial Intelligence Journalism for Research and Forecasting (AIJRF), (July, 2021), UAE.
  • Mohamed Abdulzaher, Artificial Intelligence Journalism: the 4IR and Media Restructuring, Artificial Intelligence Journalism for Research and Forecasting (AIJRF), Dubai, 2019.
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