Content Personalization is the most revered keyword in the digital world today, where content is the king, and technology serves as the impeccable tool! All businesses across industries have some form of virtual presence- websites, social media handles, etc. Content exists indispensably everywhere. The digital world feeds on content and people are subconsciously and deliberately, both ways, being driven by the immense power of content in their day to day life. Content personalization plays an important role here. Your customers will love to see content that is personalized to their likes, dislikes, preferences. And as personalization is quite predictive once you know your customers (target audience), you can devise mechanisms exclusively catering to their unique needs. Machine Learning has started playing a key role here. As Bill Gates has said so aptly“If your business is not on the internet, then your business will be out of business.”And, if your business has adopted digital technology capability in the form of ML, you are above par, and perfectly driving along the digital line!ML  is helping marketers to scale their content marketing strategies ina big way!

Let’s see how Machine Learning  AI Capabilities are enabling content personalization at length, leading to organizations’ ability to scale and enhance their customer experience!

Optimization of Content Delivery

Optimizing means locating the essential points in a customer’s journey and adding a personal touch to them. Contexts play a trigger to the need of creating and optimizing specific content between customers. Machine Learning algorithms help deliver the right content at the right time for innumerable individual website visitors based on their data about what they have done in the past. Along with NLP, ML   does scanning of content, first, then goes deeper to it and understand core meaning alongside the context, and then end up indexing, building a custom library for specific usage. This facilitates the automated delivery of personalized content across the web, email, or desktop and mobile platforms.

Boosting Content Efficiency

What is your topmost priority in your wishlist while sending emails to target audiences? Of course, content engagement. How to assure increased content engagement metrics from your target audience? The email content is thriving even though chat apps and social media channels have emerged as popular user interface modes. Email marketers are heavily relying on Machine Learning capabilities for content personalization and relevance. The former use ML to segmentize markets, as well as for market-timings and copywriting aspects of email marketing. The ML technology boosts the overall effectiveness of content efficiency in email marketing. Just that you have to identify the most suitable Machine Learning email marketing tools for your business. So, you see, Machine Learning capabilities take care of boosting the efficiency of the billions of emails sent and received per day. As per a recent Statista Report, around 306.4 billion emails have been sent and received in the erstwhile year 2020, this figure is expected to rise to 361.6 billion-mails on daily basis by 2024.

Individualized Content-Experience

Active content users are those users who give more time to content and have a higher rate of engagement on a particular landing page(s). Traditional website metrics such as the number of page views, number of sessions are not sufficient for an individualized experience. Deep content analytics, powered by  Machine Learning algorithms,  go beyond this traditional type. They focus on all those metrics that provide real insights on content engagement like those active users just we spoke about!ML analyzes these data at scale, on a real-time basis, based on the exact momentous content engagement patterns of users. This way, AI, ML capabilities powered personalized tools allow the individualization of content as they use these insights for automating cross-channel varied content distribution to web users as per their unique interests, preferences, and behaviors.

You have the data! What next?

The Basic Algorithms and Advanced Algorithms features of ML  lead to the ultimate Content Personalization of users visiting various websites and social media platforms. The  Predictive Content Personalization Engines collaborate with the Data Management Platforms and together they perform activities like syncing, merging, and segmenting data. Consequently, user-specific content promotions, messaging recommendations, etc. are executed and delivered.

Notable examples – Netflix, Amazon, Spotify

ML-based Predictive Content Personalization goes hand in hand with the e-commerce industry, entertainment industry, and so on. Amazon leverages the massive potential of e-mail recommendations and says that this is proving to be more efficient than on-site personalization. Netflix uses dynamic page metrics and follows recommendations generated by ML that are unique to visitors’ tastes and preferences. Spotify leverages the power of ML-backed personalized playlists such as Discover Weekly, Release Radar, Spotify Radio, and some new ones, too, all emphasizing the content personalization aspect.

Tighten your seatbelt– Content Personalization is the key to your business

Predictive content personalization, powered by Machine Learning algorithms enables an organization to provide a unique experience to each user, visitor, or prospective customer. Real-time high-quality recommendations, personalizing every touchpoint along a customer’s journey while safeguarding data privacy and security aspects, ML helps content personalization in a way that dramatically boosts the chance of conversion rates. That’s what your ultimate business goal, isn’t it?

We offer a wide range of customer-Centric Machine Learning Services to deliver personalized content for your audience, that can integrate well with your marketing communications or with existing applications.

In this digital era, the terms like Virtual Reality, Augmented Reality, Artificial Intelligence, and Machine Learning are thrown in more often. These four two letter acronyms are the futuristic technologies that are being used in the development of lots of tech products. Most of us don’t know the real meaning of these terms. Though, all four have brought some huge changes in the world of technology, the question “Which Technology can pave the way for the future” has been nagging our minds for a long time. Hope this article gives you a clear idea of how these technologies work and aid you understand their contribution to various fields.

The ‘big four’ are:

• AI – Artificial Intelligence
• ML – Machine Learning
• AR – Augmented Reality
• VR – Virtual Reality

Artificial Intelligence (AI)

Let us discuss about one of the revolutionary world changing term Artificial Intelligence. The term coined by John McCarthy is considered the father of AI  in 1955. In fact, AI is the big umbrella under which machine learning and deep learning which are being used for various applications in the market. Deep learning is the latest innovation in AI. AI has a gigantic effect on your life, whether you’re aware of it or not, and its influence is likely to grow in the coming years.

What Are The Uses of AI?

Today AI is ubiquitous, used to recommend what you should buy next online, to recognize your voice and understand what you say to virtual assistants such as Amazon’s Alexa and Apple’s Siri, to recognise who and what is in a photo, to spot spam, or detect credit card fraud etc.

A website like Amazon.com uses AI to suggest products that you could like. Facebook tells you whom you could know and want to be friends with. iOS’s Siri, and Windows Mobiles’ Cortana are all intelligent digital personal assistants which help us to find handy information when you ask for it using your voice; you can say “Where’s the nearest Coffee shop?”, “What’s on my schedule today?”, “Remind me to call John at seven o’clock,” and the assistant will respond by getting information, communicating information from your phone, or sending commands to other apps. Siri completes your tasks. Google lists search results based on your location. At the start of this year, the Washington Post reported  an algorithm that was developed by Google to potentially let self-driving cars learn to drive in the same way that humans do, through experience. These programs are all active applications of AI.

Microsoft says that Cortana “continually learns about its user” and that it will ultimately develop the skill to anticipate users’ needs. Virtual personal assistants process an enormous amount of data from a variety of sources to learn about users and be more active in helping them consolidate and track their information.

Many smart home devices now include the ability to learn your behaviour patterns and help you save money by adjusting the settings on your thermostat or other appliances in an effort to increase convenience and save energy. AI is everywhere, and it’s making a huge difference in our lives every day.

Machine Learning (ML)

Now let’s see “what is Machine Learning?”, “The use of Machine leaning” and “how does it work in our day today life?”.

Machine learning is a similar concept of AI. The term machine learning was coined by Arthur Samuel in 1959. Machine Learning is a division of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Machine learning makes use of algorithms to build analytical models, assisting computers “learn” from data.

Detection of network intruders working towards a data breach, email filtering, optical character recognition,  and computer vision are all ML applications.  Machine learning is so ubiquitous today. Many researchers also think that it is the best way to make progress towards human-level AI.

AI, as the idea that machines can act intelligently, is an extensive concept. Machine learning is an idea that machines should be able to learn themselves to get access to enough data. This might sound like the territory of geeks, but it could also be a way to deliver personalised recommendations and solutions in the future.

Augmented Reality (AR) & Virtual Reality

Tim Cook, CEO, Apple said “I’m excited about Augmented Reality because unlike Virtual Reality which closes the world out, AR allows individuals to be present in the world but hopefully allows an improvement on what’s happening presently… That has resonance.”

AR and VR are very popular in these centuries. Bu the terms can be confusing. Sometimes few think AR and VR are but same.

Let us discuss about importance of AR and VR as well as how does they influenced our day to day life? Augmented reality (AR) is a technology that widens our physical world, merging layers of digital information with the user’s experience in real time. It is Boeing researcher Thomas Caudell who first coined the term Augmented Reality in 1990.

Virtual Reality (VR), by use of computer modelling and simulation, connects the user with an artificial three-dimensional (3-D) visual. In 1987, Jaron Lanier used the term ‘Virtual reality’ for the first time.

How do Augmented and Virtual Realities Differ? Virtual reality provides a digital rebirth of a real-life pulses, while augmented reality delivers virtual elements as a connect to the real world. Augmented reality is being used more and more in mobile devices such as laptops, smart phones, and tablets to change how the real world and digital images, graphics intersect and interact. Whereas in Virtual Reality is usually brought to the user through a head-mounted, or hand-held controller. This equipment establishes a connect between people and virtual reality, allows both to control and navigate their actions in an environment meant to simulate the real-time world.

Let us see, what are the main similarities between AR and VR? In terms of technology, they each exist to serve the user with an enhanced or enriched experience.

With virtual reality, you can swim with sharks. And with augmented reality, you feel a shark coming out of your screen.

When more freedom and promises are made possible for marketers because it does not need to be a head-mounted display.

Virtual Reality and Augmented Reality are the key examples of experiences and interactions enabled by a deep immersion into a simulated land for entertainment and play, or to add a new approach of interaction between digital devices and the real world.

It’s 2018, there’s no doubt about the fact that the world is aware of the wonders one can of do with Artificial Intelligence and Machine Learning.We are living in exciting and innovative times with futuristic technology literally at our fingertips.

How machine learning can help ones business is the question that is on every entrepreneur’s mind. We’ve got to the stage where we take intelligent machines for granted. We trust them to carry out complex tasks requiring extreme precision, speed, and accuracy – from pacemakers to auto-pilots. But while autonomous cars get all the headlines, it’s actually many other industries that may feel the greatest impact of the new tech. So,it’s very intriguing to learn howmachine learning improve businesses intelligence.

Put simply, machine learning is about understanding data and statistics. It’s a technical process where computer algorithms find patterns in data, then perform functions – like improving the core functionality of existing software and analytic, uncovering previously inaccessible insights hidden in large data sets unstructured data formats, and taking over tasks like image recognition, text analysis, and repetitive knowledge work. The potential use cases are seemingly endless, from supply chain and risk detection to logistics and technical support to behavioural analysis and customer support.

Businesses can also use machine learning to up-sell the right product, to the right customer, at the right time. In 2018, marketers will continue to rely on machine learning to understand open rates when it comes to email– so you know exactly when to send your next campaign.

Retail companies are also tracking what ads or images you’re most likely to stop scrolling on, in order to target you with specific content. Machine learning is applied to reduce the risk of credit fraud in small businesses. Machines learn from historical datasets that contain fraudulent transactions and can identify patterns that represent a typical fraudulent transaction.

The value that machine learning can deliver will be dependent on the degree to which these systems can deal with structured and unstructured data and its quality. Similarly, Chatbots can also help achieve a faster customer service resolution, as well as provide quick histories of each customer for impeccable customer service.

Consequently, machines will not eliminate human intervention in decision making. Machines will simply change the nature and timing of our intervention. A machine self-learns so that the algorithms can predict accurately. However, final decisions have to be taken by living, breathing, empathetic entities.

To know more, Please Visit us.