Prediction considering the data from the past is now part of our lives. Gone are the days where people had no clue about the weather of the next few days or Facebook could suggest relevant news or products to buy. Well, the times are changing and with AI and machine learning, it is moving fast.
Work, daily routine tasks, technologies including huge business decisions are based on the automatic analysis. All of this software is working on the principles of Machine learning. If you are noticing right now, you might also observe the changes happening after the pandemic.
Points to discuss:
· How pandemic has changed online media and publishing?
· Why hyper-personalization is an important post-pandemic?
· How Machine learning is helpful for marketing & publishing?
· How can we help you achieve your customer goals?
How Pandemic has shifted online media and publishing?
Marketing, eCommerce, and retail businesses are depending on the bots now and it was even before the pandemic, but it is now a necessity. COVID-19 has a huge impact on consumer behavior. People are relying on online platforms for essential shopping, socializing, remote work, and the list goes on.
Initially, consumer behavior during a pandemic can be counted as panic shopping. People were only purchasing essentials and valuable things. The behavior is certainly different among the countries. The percentage of the change in mindful shopping varies in countries such as the US became 40% more mindful, china 32% and Germany 40% and other countries have a different percentage.
Similarly, food and groceries are more in demand whereas travel and transportation are quite low and movies or TV shows in the middle. However, by the time companies are learning and making adjustments to provide according to the needs of the customers if they have to survive.
Now, all this data analysis is possible with hyper-personalization technology.
But what is hyper-personalization and how it is different from traditional personalization?
Personalization VS Hyper-Personalization
It is the way companies offer 1–2–1 service to their potential customers. It includes the deep analysis of user behavior from search history to the previous purchases or recently viewed content. Well, it takes heavy data processing for gathering this amount of data from images, videos, scrolling, and website content.
Hyper personalization differs as it provides data from immediate actions by the user. It can even analyze the weather to predict the urgent need for a customer. The real-time data using machine learning tune the more relevant content. You can check below the building of a segment on one at scale for the hyper-personalization framework.
Why hyper-personalization is important during and post-pandemic?
It is essential as every new or old organization is struggling to reach out to the audience through authentic marketing strategies. To make it possible, you need hyper-personalization as it will improve the response and help you generate more views. Moreover, one can always optimize their budget considering the data of a consumer in depth.
However, here is a little reality check that ML was always important, and in terms of personalization, it was already in implementation. Pandemic has just accelerated it. It is needed more than ever now as the market is shifting to automatic solutions for almost every requirement of life.
One thing that will remain common post-pandemic in marketing is relevance, trust, and engagement. Now, to succeed in it, you need to establish a data-driven business and use the AI/Machine learning-based technology to cultivate the culture of automation.
How Machine learning is helpful for marketing & publishing?
Data-driven companies have more impact on their customers than any others. They can reach out on time and with more relevance to the people and offer a better recommendation for the next purchase and it is all possible with hyper-personalization technology usage.
Here are some of the Real-time examples of hyper-personalization machine learning:
· Image recognition.
· Speech recognition (google voice search is the top example).
· Prediction (Microsoft technology solutions for HR and other departments or Statistical Analysis System (SAS)).
· Financial and banking sector.
All of these technologies are already in implementation and used by various companies such as Spotify, Google, Amazon, etc.
How can a company help you achieve your customer goals?
Any machine learning company can turn your dull and simple business ventures into an automatic solution for your customers. So, if you are looking for solutions to transform your business with AI and machine learning solutions, start finding one.