Apr 28, 2021

Ogury reveal Personified Targeting, an AI-powered technology

AI
Advertisments
Targeting
Data
Tilly Kenyon
2 min
Ogury has announced the release of Personified Targeting, a ‘breakthrough’ AI-powered technology for audience targeting on mobile web
Ogury has announced the release of Personified Targeting, a ‘breakthrough’ AI-powered technology for audience targeting on mobile web...

Ogury, a global technology leader in mobile brand advertising, has announced the release of Personified Targeting. This is an AI-powered technology that brings the company's unique, in-app audience data onto the mobile web, for highly relevant mobile web targeting, without the use of any personal identifiers.

Ogury's Personified Targeting addresses issues that advertisers have come across year after year when trying to increase their brand awareness. Companies often try semantics and contextual targeting, but these technologies are not very precise and lack audience intelligence. Advertisers can also target individuals through behavioral tracking, but this can be invasive. 

Personified Targeting ‘works seamlessly across both app and web environments’ and enables precise reach with relevant audiences, without the use of any user data, identifiers, or device graphs, making it fully respectful of consumer privacy and future proof.

“We’ve expanded our reach and become a cross-environment mobile player with the addition of exclusive, in-web Thumbnail ads,” said Sarah Jones, senior director, global product marketing at Ogury. “With Personified Targeting, our advertisers’ campaigns can leverage our unique and powerful in-app audience data for in-web targeting. That means precise reach with relevant audiences across environments, with total user privacy protection land a future-proof execution – no compromises.”

How does it work?

According to Ogury Personified Targeting is fueled by unparalleled personification data. Personification data combines best-in-class contextual and semantic data with unique and powerful audience data at scale. Ogury's audience data gives a deep understanding of publishers' audience’s behaviours and accurate insights into their interests. It is founded on 6 years of proprietary mobile journey data, and continually validated and enriched with:

  • Survey responses at scale, generating zero-party, self-declared data
  • User ad choices and interactions, providing self-targeting data

Ogury's personification data, mapped to the app or webpage, qualifies the audience of millions of apps and websites and grows richer with each ad served and every survey answered in a highly powerful virtual circle. It is used to personify impressions and enables accurate impression-centric targeting, called Personified Targeting.

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Jun 15, 2021

The advantages and disadvantages of AI in cloud computing

AI
CloudComputing
Data
ML
3 min
AI is being used in cloud computing, which works by allowing client devices to access data over the internet remotely, but are there pros and cons?

Cloud computing offers businesses more flexibility, agility, and cost savings by hosting data and applications in the cloud. AI capabilities are now combining with cloud computing and helping companies manage their data, look for patterns and insights in information, deliver customer experiences, and optimise workflows.

We take a look at some of the benefits and drawbacks of AI in cloud computing. 
 

The benefits of AI in cloud computing

 

Lower costs

A major advantage of cloud computing is that it eliminates costs related to on-site data centers, such as hardware and maintenance. Those upfront costs can be restrictive with AI projects, but with cloud enterprises you can access these tools for a monthly fee, making research and development related costs more manageable. AI tools can also gain insights from the data and analyse it without human intervention, reducing staff costs.

Deeper insights 

AI is able to identify patterns and trends in large data sets. Using historical data, AI compares it to the most recent data, which provides IT teams with well-informed, data-backed intelligence. AI tools can also perform data analysis fast so enterprises can rapidly and efficiently address customer queries and issues. The observations and valuable advice gained from AI capabilities result in quicker and more accurate results.

Improved data management

AI enables extensive data management, and cloud computing maximises information security, making it possible to deal with massive amounts of data in a programmed manner to analyse them properly, allowing the business to leverage information that has been “mined” and filtered to meet each need. AI can also be used to transfer data between on-premises and cloud environments. 
 

Intelligent automation 

Businesses use AI-driven cloud computing to be more efficient and insight-driven. AI can automate repetitive tasks to boost productivity, and also perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows. IT teams can focus more on strategic operations while AI performs the mundane tasks. 

Increased security 

With businesses deploying more applications in the cloud, security is crucial in order to keep data safe. IT teams can use different AI-powered network security tools which can track network traffic, they can flag issues, such as finding an anomaly. 
 

The drawbacks of AI in cloud computing

 

Data privacy 

 Enterprises need to create privacy policies and secure all data when using AI in cloud computing. AI applications require a large amount of data, which can include consumer and vendor information. While some data can be anonymous and can't be tied to personally identifiable information, knowing who the data belongs to makes it more valuable. When sensitive information is used, data protection and compliance is a major concern.

Connectivity concerns 

IT teams use the internet to send raw data to the cloud service and recover processed data. Poor internet access can hinder the advantages of cloud-based machine learning algorithms, as cloud-based machine learning systems need consistent internet connectivity. 

While processing data in the cloud is quicker than conventional computing, there is a time lag between transmitting data to the cloud and receiving responses. This is a significant issue when using machine learning algorithms for cloud servers, where prediction speed is one of the primary concerns.

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