As digital advertising adapts to increased user privacy and stricter rules, the question arises: How do we target the right users without relying on third-party data? Since the traditional methods based on tracking online users are hindered by privacy requirements, advertisers and brands are now exploring new approaches that respect privacy while ensuring relevance and personalisation, which are key to success. For years, iPROM has pioneered alternative targeting methods that do not require the use of data from a third party. In this blog, we will focus on three primary methods shaping today’s digital advertising landscape.

Why are alternative targeting methods gaining traction today?

Traditional targeting relied heavily on third-party data collected via cookies and other tracking tools, allowing advertisers to serve ads to specific users based on the history of their online behaviour. However, with the introduction of new regulations and the increasing number of browsers that block third-party cookies, there is a need to move to other targeting methods.

Reaching users with the power of contextual targeting

While not a new concept in digital advertising, contextual targeting is experiencing a resurgence as one of the most important targeting strategies in an era of emphasis on user privacy. This method moves away from traditional targeting based on user behaviour and focuses on analysing web page content.

It is considered highly effective because it places ads in digital media that include content directly relevant to the product or service being advertised. This is demonstrated by a practical example where the combination of contextual targeting and the iPROM Spider Ad™ solution on the Mozaik portal, significantly improved user engagement – average session duration increased by 70%, page views rose by 54% and session duration surged by 73% compared to other traffic.

The power of semantic analysis for a deeper understanding of the content of the medium

Semantic targeting is an advanced upgrade of contextual targeting that provides an even better understanding of the meaning of content. Using natural language processing (NLP) and machine learning technology, we can carry out a detailed analysis of media content to discern the context, tone, emotion and intent. This analysis goes beyond simply identifying keywords and provides a comprehensive understanding of the content a user is engaging with.

At iPROM, we used these technologies to promote Murska Sobota and the Expano pavilion, achieving a 50% increase in visits to the pavilion over the summer. Also, accommodation capacities in the Pomurje region were almost fully booked during the summer.

In the future, we expect this method to be further enhanced by the use of Large Language Models (LLM) and Generative Artificial Intelligence.

First-party data as the basis for personalised targeting

First- party data collected directly from users through a brand’s own media properties is emerging as the basis for advanced advertising strategies. Using a first-party data management platform, such as iPROM Private DMP, companies can generate insights that can be used to create highly personalised and relevant advertising campaigns that closely match the needs and interests of their target audiences.

Lisca increased online sales of swimwear on the Croatian market eightfold with a digital media targeting and buying strategy based on first-party data. Encouraged by the success of this strategy, Lisca plans to gradually roll out similar strategies in other markets. Similarly, SKB Bank increased advertising effectiveness by 72% and reduced cost-per-reach by 25% by using iPROM Private DMP and first-party data on its digital audience.

As digital advertising evolves, it is essential that we continue to strike a balance between ensuring user privacy and delivering advertising effectiveness that benefits both businesses and consumers. In the second instalment of this blog series, I will delve deeper into complementing these targeting methods with targeting strategies that leverage data collected directly from users.