How contextual data is revolutionizing advertising

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Advertising has come a long way in the last few decades. With the rise of digital marketing, advertisers have access to more data about consumers and businesses than ever before. This data is fed by vast new computing power, resulting in increasingly effective ways for advertisers to deliver messages.

Step into the next generation of advertising technology. This new wave of technology combines AI and contextual data select ads tailored to consumers on an individual level. By analyzing data about a person’s interests, preferences, and behaviors, advertisers can deliver content to target audiences that resonates at very specific times.

The key to this new approach is contextual data. Instead of simply looking at a person’s demographic information or search history, advertisers are now looking at a person context — where they are, what they are doing and what interests them, measured in real time across thousands of data points. By understanding a person’s context and automating the creation of personalized content in seconds, advertisers can deliver ads to millions of consumers simultaneously that are highly relevant.

By using machine learning algorithms, AI can analyze vast amounts of data to identify patterns and insights that are impossible to manually monitor and act on.

Related: How new age technologies are changing the ad tech industry

Here’s how each of these technologies plays a role in generating highly personalized content for each individual:

• Machine learning: Machine learning algorithms allow AdTech companies to analyze vast amounts of data about each user, including their browsing history, search queries, social media activity, and other interactions. These algorithms use this data to identify patterns and make predictions about what content is most relevant and attractive to each user.
• Predictive Analytics: Predictive analytics is the use of statistical algorithms and machine learning techniques to analyze data and make predictions about future events or behavior. In AdTech, predictive analytics is used to anticipate users’ needs and preferences even before they express them. By analyzing patterns in user behavior and other data points, AI algorithms can make highly accurate predictions about what content will be most engaging and relevant to each user.
• Natural Language Processing (NLP): NLP is a branch of AI that allows computers to understand, interpret and generate content in the human voice. By using NLP, AdTech companies can analyze and generate content that is highly curated and tailored to the interests and needs of individual users. This technology allows computers to understand the nuances of human language, including context, intent, and sentiment, which is essential for generating highly personalized and relevant content.

Imagine a world where you’re walking down the street and you get a notification on your phone of a nearby coffee shop that you haven’t tried before. The notification is customized to your interests and preferences, as it is historically the type of coffee you like, at the prices you typically pay, set in an environment you tend to enjoy in a coffee shop, at the time of day you typically drink coffee when Out and about. The notification also includes a discount for a drink you have purchased in the past. This is an example of artificial intelligence and contextual data working together to deliver a highly targeted, personalized ad.

But this approach is not without its challenges. There are obvious concerns about privacy and the ethical implications of using personal data to target consumers.

Although politicians have taken an active position in regulate the industry Through the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) in the United States, keeping statutes up to date in this rapidly evolving ecosystem poses a challenge, to say the least. less. In the near term, transparency will ultimately dictate effectiveness for both advertisers and end consumers as we move closer to a convergence point in value and derivatives.

Related: Protecting digital identities: why data privacy should matter to you (and your business)

Despite these challenges, the benefits of this engagement approach are significant. Resolving relevance and timing creates a win-win for all stakeholders across all verticals in consumer and business.

Every second that passes represents millions of recorded data, especially in advertising. This directly correlates to improving models and algorithms in a positive feedback loop leading to the overall growth ideal of personalized advertising, now only the beginning of what can only be related to a “curve curve” exponential growth story. J” for industry. and the underlying technology.