Targeted Advertising using Machine Learning

Targeted advertising is a form of online advertising which micro-targets its customers. It is based on the traits and behavioral patterns of different people. Nowadays, people, knowingly or unknowingly, are churning out personal data at an unprecedented scale because of the use of all electronic devices. Simply stated, if the device has an internet connection, then the device IS leaking personal information to advertisers. Targeted advertising has been gaining importance ever since the start of the century. This is because people are becoming more and more diversified and it is not feasible or even possible to fit them all into one campaign. Thus, organizations today are turning towards the concept of Targeted advertising as a way to boost their revenues.👁 Targeted-Advertising-using-Machine-Learning
To get an insight into a person's behavior, one only needs to analyze their internet activities. Today's population is majorly dependent on the internet for solutions to practically any problem they face which can range from "What TV series should I watch today?" to googling their symptoms before even visiting a doctor. If data is generated about such internet activities, then the advertisers can virtually know their customers at a personal level and thus advertise to them according to their needs. This is precisely what organizations are doing today.

The Role of Tech Giants

This process has become so profitable that software giants like Google and Facebook earn a major part of their revenue by micro-targeting their users and advertising their clients' products. Google has also been known to deploy a selective filtering feature for its clients in which the Google Search Algorithm has a bias toward the clients' products. This feature also has the potential to influence elections and thus can be considered to be more powerful than the US president himself.

Facebook’s Tracking Practices

Facebook has garnered a reputation as an "obsessive stalker" because of its obsession to track its users' every movement. Facebook generates insights about its users by tracking the following -

1. Location Data: This is probably the easiest to track for the company>. Even if the setting is disabled, Facebook can still track people by using IP addresses.
2. Mouse Movements: This shocking revelation was made in Mark Zuckerberg's testimony to the state. Using the mouse movements and other interaction data points, it gets an insight into the type of content the user interacts with the most.
3. Connected Applications: Facebook collects data from the other applications it owns like WhatsApp and Instagram to get more insight into its users.

The infamous Cambridge Analytica scandal was the birth child of the concept of Targeted advertising. It is a common saying that "If you are not paying for the product then, You are not the Customer, YOU are the product"

Applications of Machine Learning in Targeted Advertising

Targeted advertising using machine learning involves using data-driven insights to tailor ads to specific individuals or groups based on their interests, behavior, and demographics. Here are some ways machine learning is used for targeted advertising:

• Audience Segmentation: Machine learning algorithms can be used to segment audiences into specific groups based on shared interests, behaviors, and demographics. This allows advertisers to create targeted ads that are more likely to resonate with specific individuals or groups.
• Predictive Analytics: Machine learning can be used to analyze data on consumer behavior and purchasing patterns to predict which users are most likely to engage with certain ads or products. This helps advertisers to create more effective ad campaigns and allocate their advertising budget more efficiently.
• Personalization: Machine learning can be used to personalize ads to specific individuals based on their browsing history, purchase history, and other data points. This allows advertisers to create more relevant and personalized ads that are more likely to convert.
• Optimization: Machine learning can be used to optimize ad campaigns in real time based on performance data. This allows advertisers to adjust their ad targeting and messaging to maximize their return on investment.
• Fraud Detection: Machine learning can be used to detect and prevent ad fraud, which occurs when advertisers pay for ads that are not seen by real users. This helps to ensure that advertisers get what they pay for and that ad campaigns are effective.

Conclusion

Overall, targeted advertising using machine learning can help advertisers to create more effective and efficient ad campaigns that are tailored to specific audiences. It can also help to prevent fraud and ensure that ad campaigns are generating a positive return on investment.