As users become more concerned about their privacy, relying solely on third-party cookies can erode trust. Using diverse signals can enhance transparency and show a commitment to user privacy.
Genelle Hung, Country Manager Southeast Asia of PubMatic, advised: “Publishers must continue adopting diverse signals beyond third-party cookies. Google’s decisions and timelines should not hinder our industry’s progress toward a superior supply chain for digital advertising across the open internet. We have seen that alternative signals can provide better outcomes for advertisers and consumers alike and help provide a more sustainable addressability strategy.”
Utilising first-party data and other signals can improve targeting and measurement. With evolving advertising technologies and data sources, publishers can leverage multiple signals to optimise ad performance and ensure better ROI for advertisers.
Hung added: “We understand that APIs must evolve in light of Google’s announcement, and we will continue partnering with our peers to inform the specifics and timing.”
Implement contextual tools
Contextual tools analyse the surrounding text, images, and metadata of a web page to understand its subject matter and deliver relevant ads accordingly. Examples include natural language processing (NLP) algorithms to determine the theme of articles, image recognition to analyse visual content, and sentiment analysis to gauge the emotional tone.
By focusing on first-party data strategies and contextual tools, advertisers can achieve targeted reach without compromising user privacy. As Fiona Salmon, Managing Director at Mantis, explained:”With regulatory bodies such as the ICO still discussing the unsuitability of cookies and with the exact nature of Google’s Privacy Sandbox remaining unclear, let’s not discount another twist in the cookie saga. Continuing to implement contextual tools and first-party data strategies should be an ongoing priority for advertisers looking to reach their audiences effectively.”
Use AI-driven predictive modelling
AI-driven predictive modelling analyses historical data and detects patterns to forecast future outcomes. It involves using machine learning algorithms to process vast amounts of data and recognise complex relationships and trends that humans might miss. This allows companies to tailor marketing strategies and make data-driven decisions while respecting consumer privacy and reducing reliance on intrusive tracking methods.
Leonard Newnham, Chief Data Scientist at LoopMe, asserted that the despite temporary extension for third-party cookies, the reality remains that the majority of consumers oppose being tracked online. He emphasised that the future of advertising is privacy-centric, urging organisations to adopt tools that ensure effective advertising performance without compromising consumer privacy.
Newnham said: “To create a true one-to-one connection with consumers, brands need to find more effective ways of reaching their audience. They should build out their data insight capabilities and use AI-driven predictive modelling to optimise their media based on multiple data signals, allowing them to improve outcomes by allocating resources in the most efficient way.”
Invest In alternative solutions
