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Publication Name: Expresscomputer
Date: March 29, 2024

AI for Ad Analytics

AI for Ad Analytics

The new advertising and marketing strategy has seen a massive shift towards digital engagement. One of the principal technologies that are being widely adopted is artificial intelligence. We have seen the tremendous implications of AI in personalised recommendations, content generation, target advertising, and so on. Specifically, in advertising, AI is playing a vital role in analysing and optimising campaigns. With machine learning algorithms, AI is aiding advertisers to predict consumer behaviour and make data-driven decisions in real-time.

Advertisers are tapping into AI tools for their continuous learning capabilities, and how they deliver strategies based on performance feedback. This further ensures optimisation and efficiency in advertising tools like ad slot forecasting. In simple terms, ad slot forecasting predicts the availability and performance of advertising inventory across platforms and media. AI and ML play a crucial role in deciding an ad slot as they analyse historical data, market trends, and other factors to anticipate ad slot availability and pricing.

AI is also vital for applying several strategies for the wide reach of an ad. For one, AI and ML algorithms analyse vast amounts of data to improve the accuracy of predictions. Incorporating real-time data feeds and monitoring tools helps adapt strategies dynamically. AI also helps in collaborating with publications and ad networks for premium placements as well as in negotiating favourable terms. Additionally, implementing A/B testing and optimisation techniques guarantees the effective allocation of advertising budgets to achieve optimal impact.

AI for audience retention

The huge volumes of content today make it difficult (and necessary) to retain the attention of audiences. With the help of artificial intelligence, advertisers can tap into predictive ratings for audience retention to forecast their engagement with said content. AI and ML algorithms analyse historical data, user behaviour patterns, users’ tastes and preferences, and other such data to help creators create bespoke content for higher retention. Nearly all streaming platforms and channels provide users the option to give feedback or write a review, which further creates more data for advertisers and content creators.

Creators and advertisers are also using AI to enhance the quality of the content produced. AI and ML tools help in audience segmentation for a more accurate targeting of the content. AI algorithms can analyse diverse variables such as demographics, behaviour patterns, and preferences to segment audiences effectively. This in-depth understanding helps in producing personalised content with a higher rate of audience retention as well as conversion.

AI plays a crucial role in ad serving, placement, and presentation. It’s essential to consider the context in which ad content is placed and presented to users. Factors such as the time of day, device type, and social occasions’ sensitivity influence the optimal presentation of ads. AI analyses these factors to recommend the most effective ad-serving strategy, minimising distractions for the target audience. This personalised approach integrates ads seamlessly with the overall content. Techniques like Virtual Product Placement (VPP) and real-time decisions on mid-roll, QR code, or L band placement are powered by AI decision engines.

AI continuously refines segmentation models through iterative learning, ensuring accuracy and adaptability to evolving consumer trends, which is also evident on social media platforms. Further, AI can dynamically adapt ad elements such as imagery, copy, and calls-to-action in real time based on user interactions, ensuring relevance and engagement. By automating this process, AI empowers marketers to deliver hyper-personalised content at scale, maximising ad performance and ROI while enhancing the overall user experience.

How AI is helping prevent ad frauds

Every innovation comes with its own set of pros and cons. In the field of advertising, there is “ad fraud”, which is the dark side of advertising. Simply put, ad fraud refers to deceptive practices aimed at manipulating online advertising metrics. This is mostly achieved with automated bots or malicious software. The aim here is to generate fake clicks, conversions, or impressions, which deceives advertisers to pay for non-existent engagement. Ad fraud can have a demeaning impact on digital advertising as it wastes advertisers’ budgets, misleading performance metrics, and degrades trust in online advertising.

That said, artificial intelligence plays a critical role in combating ad fraud. Advertisers can leverage advanced AI and ML algorithms to detect and prevent fraudulent activities in real-time. Furthermore, AI helps in the continuous analysis and monitoring of incoming data, which helps in identifying fraudulent patterns like unusually high numbers of clicks, suspicious user interaction, or inconsistencies in traffic sources.

Moreover, machine learning models can help predict and flag potential frauds with high accuracy as they are fed with historical data. By proactively identifying and intercepting fraudulent activities, AI helps advertisers maintain the integrity of their campaigns, preserve budgets, and safeguard the effectiveness of online advertising ecosystems.

In conclusion, AI tools can be effectively utilised to get the best of digital advertising. We know that AI plays a crucial role in delivering personalised content, which further helps in increasing ad performance and ROI. AI also helps advertisers tailor messaging and cultivate stronger connections with the target audience, further driving growth. Ultimately, the role of AI and ML is still being explored, and its potential to unearth more possibilities is to be ascertained.


Author: Biswajit Biswas, Chief Data Scientist, Tata Elxsi