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Artificial Intelligence is the ultimate weapon in the fight for the interest of millennials. But are they really such a difficult generation that marketers are forced to resort to AI to figure out what they want?
Millennials (as those born between the 1980s and early 2000s are called ) are a demographic so popular that marketing managers around the world are vying for its attention.
By 2025, the millennial generation will account for 75 percent of the world's working-age population, and its total purchasing power now exceeds $ 200 billion a year. Fortunately, advances in machine learning are helping to understand what this fickle "target market" really wants.
Machine learning (ML) analyzes existing data and uses complex algorithms to predict what will happen in similar cases in the future. Fortunately, the sheer amount of time millennials spend on the Internet provides ample information for that.
ML examines the resulting data on purchases, social media activity, or any other online activity. All of this can be used to successfully target ads, as traditional marketing techniques don't work with millennials. New approaches are required, which you will learn about in this article.
Approach 1: Internet of Things (IoT)
The number of online shopping platforms has skyrocketed with the advent of the Internet of Things. We are no longer limited to laptops and smartphones. Almost all of our activities can be tracked using devices that are used in everyday life: smart TVs, fitness trackers, watches, GPS key fobs and much more.
In the concept of the Internet of Things, everything we do on the web is converted into data. This data can be analyzed using Artificial Intelligence and used to identify patterns in the daily lives of millennials. Knowing how millennials live provides insight into how best to attract their attention to your product.
According to the Economist Intelligence Unit, three quarters of companies (75%) are either actively exploring IoT or are already using it, and three years later, almost all executives (96%) plan to introduce the Internet of Things into their IoT business.
Approach 2: Social Media
Artificial intelligence is a key component of popular social networks. Facebook uses advanced machine learning to do everything from content delivery and facial recognition in photos to targeted ads. And on Instagram, AI-powered screen readers can describe photos.
Snapchat uses the power of computer vision, Artificial Intelligence technology to analyze facial features and apply filters that move with the person in real time.
These are just a few examples of how Artificial Intelligence works behind the scenes of the world's most popular social media. Also AI and machine learning regulate how generated content and purchased ads are presented to users - often in ways that are not entirely transparent to marketers.
Social media data mining is one of the most effective ways to collect information about millennials. They are more connected to social media than any other age group. Using machine learning to collect social media data allows companies to determine what millennials say about a brand, how they feel about a particular product category, how they respond to competitor campaigns, and a host of other data that can be used to design targeted ad campaigns.
Approach 3: Credit Profile
The challenge that financial institutions face when underwriting millennials is their limited credit history. As a result, they are denied a loan. In this way, financial institutions minimize their risks, but also reduce the profit growth they need in today's competitive market. Traditional underwriting works well for evaluating borrowers with a long credit history, but with limited data it is impossible to distinguish between creditworthy applicants and those with high risk levels. Machine learning fills in these gaps.
Artificial Intelligence can analyze huge amounts of received data, for example, data from support, payment history and transactions. Machine learning can also add non-traditional variables to the credit profile, for example, how a client fills out a questionnaire, how he navigates the lender's website, etc.
Overall, the millennial generation will remain a target market to focus on for many years to come. However, their constantly changing behavior will make it difficult for retailers and service providers to meet their requirements.
Dealing with millennials is challenging given the sheer volume of information you have to compete with. AI machine learning is key to understanding how millennials conduct their daily lives and how they will react to products, events and new trading platforms.
Big Data and machine learning algorithms are the way to the future.
Millennials (as those born between the 1980s and early 2000s are called ) are a demographic so popular that marketing managers around the world are vying for its attention.
By 2025, the millennial generation will account for 75 percent of the world's working-age population, and its total purchasing power now exceeds $ 200 billion a year. Fortunately, advances in machine learning are helping to understand what this fickle "target market" really wants.
Machine learning (ML) analyzes existing data and uses complex algorithms to predict what will happen in similar cases in the future. Fortunately, the sheer amount of time millennials spend on the Internet provides ample information for that.
ML examines the resulting data on purchases, social media activity, or any other online activity. All of this can be used to successfully target ads, as traditional marketing techniques don't work with millennials. New approaches are required, which you will learn about in this article.
Approach 1: Internet of Things (IoT)
The number of online shopping platforms has skyrocketed with the advent of the Internet of Things. We are no longer limited to laptops and smartphones. Almost all of our activities can be tracked using devices that are used in everyday life: smart TVs, fitness trackers, watches, GPS key fobs and much more.
In the concept of the Internet of Things, everything we do on the web is converted into data. This data can be analyzed using Artificial Intelligence and used to identify patterns in the daily lives of millennials. Knowing how millennials live provides insight into how best to attract their attention to your product.
According to the Economist Intelligence Unit, three quarters of companies (75%) are either actively exploring IoT or are already using it, and three years later, almost all executives (96%) plan to introduce the Internet of Things into their IoT business.
Approach 2: Social Media
Artificial intelligence is a key component of popular social networks. Facebook uses advanced machine learning to do everything from content delivery and facial recognition in photos to targeted ads. And on Instagram, AI-powered screen readers can describe photos.
Snapchat uses the power of computer vision, Artificial Intelligence technology to analyze facial features and apply filters that move with the person in real time.
These are just a few examples of how Artificial Intelligence works behind the scenes of the world's most popular social media. Also AI and machine learning regulate how generated content and purchased ads are presented to users - often in ways that are not entirely transparent to marketers.
Social media data mining is one of the most effective ways to collect information about millennials. They are more connected to social media than any other age group. Using machine learning to collect social media data allows companies to determine what millennials say about a brand, how they feel about a particular product category, how they respond to competitor campaigns, and a host of other data that can be used to design targeted ad campaigns.
Approach 3: Credit Profile
The challenge that financial institutions face when underwriting millennials is their limited credit history. As a result, they are denied a loan. In this way, financial institutions minimize their risks, but also reduce the profit growth they need in today's competitive market. Traditional underwriting works well for evaluating borrowers with a long credit history, but with limited data it is impossible to distinguish between creditworthy applicants and those with high risk levels. Machine learning fills in these gaps.
Artificial Intelligence can analyze huge amounts of received data, for example, data from support, payment history and transactions. Machine learning can also add non-traditional variables to the credit profile, for example, how a client fills out a questionnaire, how he navigates the lender's website, etc.
Overall, the millennial generation will remain a target market to focus on for many years to come. However, their constantly changing behavior will make it difficult for retailers and service providers to meet their requirements.
Dealing with millennials is challenging given the sheer volume of information you have to compete with. AI machine learning is key to understanding how millennials conduct their daily lives and how they will react to products, events and new trading platforms.
Big Data and machine learning algorithms are the way to the future.
