What is Artificial Intelligence?

 Image-What is Artificial Intelligence?
Image-What is Artificial Intelligence?

Artificial Intelligence is an extensive branch of Computer Science. The goal of creating expert systems that can function intelligently and independently implementing human intelligence in machines answers the query ”What is Artificial Intelligence?” In Digital Marketing predict your users’ action using customer data, machine learning, etc. AI can handle large amounts of data allowing digital marketers to segment audiences and better perform online campaigns. After determining your target audience create a personal experience for these users. Serve customized content after consuming data from the web, social media platforms, and emails to boost ROI. Use AI in your digital marketing campaigns to understand your customers by forecasting trends & sales, chatbots and speech recognition, lead and content generation, dynamic pricing to rope in customers, and AI-advertising for automating ‘sell’ and ‘buy’ online advertisements. Leverage AI applications by integrating it into your workflows and transform the way digital marketing works.

Artificial Intelligence Terminologies

The below terminologies of Artificial Intelligence has been evolved from the essential reasoning of what activities intelligent humans typically perform.

Speech Recognition

Humans can talk and listen and communicate through a language.

Statistical Learning

Much of the Speech Recognition is statistically based.

Natural Language Processing

Humans can read and compose messages in a language

Computer Vision

Humans can see with their eyes and process what they see.

Convolutional Neural Network

If we get the system to scan images from left to right, start to finish it is a CNN.

Recurrent Neural Network

Humans can perceive the past. We can get a neural system to recall a constrained past

Neural Network

The human mind is a system of neurons and we can utilize this to learn things. In the event that we can repeat the structure and capacity of the human brain, we may have the capacity to get psychological abilities in machines

Profound Learning

Neural Networks are more mind-boggling and more profound and we utilize these to learn more intricate things. There are diverse kinds of Deep Learning in machines which are basically unique strategies to repeat what the human mind does.

Object Recognition

Computer Vision falls under a symbolic way for computer processing data. People perceive the scene around them through their eyes which creates images of that world. This field of image handling however not identified with AI is required. CNN is utilized to perceive objects in a scene. Computer Vision fits here and objects recognition is achieved through AI.


Humans can comprehend their condition and move around smoothly.

Pattern Recognition

Humans can see patterns, for example, a grouping of like articles.

Two Ways AI Works

Two different ways Artificial Intelligence works are Data based which is called Machine Learning and Symbolic based.

Machine Learning

We need to feed the machine lots of information before it can learn eg- if you have lots of data for sales vs seasonal products, you can plot that data to see some kind of pattern. If the machine can learn this pattern then it can make predictions based on what it has learned. Humans can easily learn in 1-2-3 dimensions but for a machine many dimensions in the 1000’s are possible. That is why machines can look at lots of high dimensional data and determine patterns. Once it learns these patterns it can make predictions that humans can’t even come close to. We can use all these machine learning techniques to one or two things – classification or prediction.

When you use some information about customers that are loyal to your brand than you are classifying that customer. If you use data to predict the possibilities of a customer that will switch to another brand than you are making a prediction.


Symbolic based

The other way for learning algorithms utilized for AI is through Supervised & Unsupervised Learning and Reinforcement Learning. Let us examine each of these.

Supervised Learning

If you prepare an algorithm with information that additionally contains the appropriate response then it is called supervised learning eg-when you prepare a robot to perceive your companions by their face and names you should distinguish them for the computer.

Unsupervised Learning

Prepare for an algorithm with information that needs the machine to guess pattern work. It is then known as unsupervised learning. eg-A advertising platform portions out the Indian populace into small groups with similar socioeconomic demographics and buying behaviors. The promoters can then achieve their target market with relevant advertisements.

Reinforcement Learning

Give any algorithm an objective and anticipate that the machine will do experimentation to accomplish that objective. This is referred to as reinforcement learning. eg: A robot can utilize a procedure known as deep reinforcement learning to prepare itself to take in another task. It takes a dig at getting objects while catching a video film of the procedure. Each time it succeeds or falls flat, it recollects how the object looked, information that is utilized to refine a deep learning model, or a large neural network, that controls its activity. To read more click on An Introduction to Artificial Intelligence.

Use Of Artificial Intelligence In Digital Marketing

AI is a tool in the hands of digital marketers that can progressively focus on customer behavior by working on insights to accelerate the growth of your brand. Analyze customer profiles and their behavior by making use of tools such as Google Analytics, Google Search Console, Google Surveys, Google Trends, YouTube Analytics, FB Audience Insights, etc. UX can be personalized by collection and analyses of user data that involve their activities such as likes, behavior, interests, demographics, and more. Website builders such as Wix is AI-powered. For e-commerce websites such as Amazon, it can provide sales forecasting & market research, product recommendations, management of inventories, logistics, customer support & services, and more. The different ways AI is impacting the world of digital marketing are listed below.

Predictive Analysis

Predictive analytics in marketing use data, AI software, and statistical algorithms to pinpoint possible futuristic outcomes by a study of users’ past behavior. For example, sales CRM software can be used to predict lead scores for classifying prospects. In such cases, the predictive analytic model can predict which of these lead prospects will result in possible buyers. Also, predictive advertising permits digital marketers to know more about their customers through their browsing history. This opens up numerous opportunities for brands to target their customers.

Content Automation

For website owners requiring a lot of content in a very short period of time, avail the services of content AI automation tools. These tools make easy the process of content – creation, curation, distribution, and analysis. A popular content curation tool is Pinterest that facilitates the creation of multiple boards on your profile. Spin Articles for your site and avoid duplicate content issues using wordai.com. Other content writing software such as Quill and Articoolo utilize machine learning for distributing content for your marketing campaigns. Create constructive content by availing content analysis tools such as Headline Analyzer, Atomic Reach, etc, and reach your audience. To find out what content works use BuzzSumo/ scoop.it. Automate keyword research using AI-powered tools such as Google Autocomplete, Google Instant, LSI Graph, Keyword Country, etc.

RankBrain uses machine learning to discern the search query or keyword. It computes how users are involved with the search result process in terms of UX. Optimize your content and make sure it is relevant to be found through voice search by focusing on long-tail keyword and user intent.

Email Marketing

AI-powered eMail Marketing tools analyze user behavior and preferences to produce results from email marketing campaigns. This can involve a gamut of tasks from growing your email lists by subscriptions, writing appropriate post titles, personalizing emails to individuals, automation of newsletters process, optimizing send times, to cleaning up your email lists. Some examples of eMail Marketing software are SendGrid, Mailgun, Amazon SES. and Pepipost.

Augmented Reality

A free AR software Adobe Aero can view, build, share immersive, and interactively augmented reality experiences. VR (Virtual Reality) is all about being immersed in a computerized, 3D environment with fake sounds and visuals that make-believe the virtual world is actually real. AR combines both VR and AR. An example of AR is the Pokémon GO app. Moreover the ManneKing app allows users to virtually try on various pieces of clothing through AR. IKEA brands use AR to allow customers to indicate how their furniture products would fit in their houses.

Image Recognition

AI-powered image recognition tools interpret, recognize, and analyze images. With the Google Reverse Image Search tool, upload an image and carry out a search with it. To search images produce the URL or the image to Google and the search engine will locate where the images have been used along with other similar types of images. Image recognition or computer vision applications understand images with the help of deep learning algorithms. For example, when you want to buy a product just click the photo of the product and use the Google Shopper App to find out other stores in the vicinity with price offers.

AI In Advertising

Use AI to comprehend how consumers react to campaigns, channels, and creatives. Advertisers can target the most qualified audience using feedback from all-inclusive media delivery that engages Machine Learning.

AI for Refining Advertisements

AI solutions can also aim at refining advertisement by assisting advertisers to find new advertising channels for their PPC campaigns. Machine Learning algorithms can find out how ads are performing across various networks. They can offer solutions to improve ad performance and include automation. Machine Learning uses real-time behavioral data based on demographic groups such as age, gender, location interests, and more to tailor personalized relevant ads. Moreover, at the back end, AI has also made a remarkable contribution to the optimization of Ads. Thus Advertisers can hope to gain an aggressive posture by exploiting channels that are not used by their competitors.

AI for Automatic Ad Creation

Many social media networks use the AI-powered systems for ad generation based on what works best for you. Automate your Facebook, Google ads using AI-powered tool eg revealbot. Their algorithms recommend what ads should be running in line with your promotion. Based on the goals you have AI systems can automate the process of creating ads. Ad copies with variations are made possible through these systems using the NLP(Natural Language Processing) and NLG (Natural language generation) processes which is better and faster than what a human can make eg Facebook and Instagram.

AI for Personalization

AI technology can avail of algorithms for pattern-based clusters to identify and match prospective users. Leverage AI and hyper-personalization for engaging users at the best possible time and match the right content for the audience. eg sizmek ad server platform. Measure the effectiveness of your campaigns and with user enabled AI systems to spot the right channels to optimize your advertising campaigns. Track performance related to both advertisers’ campaigns and competitors. eg Pathmatics- AI digital advertising platform.

AI for Optimization Ad Spends

AI Situational awareness is the key to adjusting such advertising spends by diverting spends to other effective alternate channels. The system can also scan content on the web and find relevant content and images that are appropriate for placement of ads eg GumGum ~ in-image advertising network services that focus on computer vision and NLP. With AI predictive analytics algorithms determine consumers that have the highest probability of taking action online.