With a cookie-free future looming, marketers in all industries are being forced to rethink their current strategies and approaches. Perhaps most importantly, this new era of advertising will require an increased focus on demand-side platforms (DSPs) that can thrive in this new and changing environment.

Since DSPs are big investments and play a pivotal role in the success or failure of campaigns, it’s critical that marketers understand exactly what they’re paying for and how different DSPs stack up against each other. .

This will increase the urgency of some important considerations, one of which is whether your core DSP is built to succeed in a cookie-free future, particularly in terms of how it leverages machine learning (ML) to audience modeling, campaign optimization and privacy. Additionally, the importance of first-party data and integrations cannot be overstated. As cookies disappear, this data is key to successful audience targeting, which is the foundation of a successful campaign with meaningful results.

Leverage machine learning (ML) for effective data analysis and audience targeting

Collecting and using first-party data is perhaps the most important element of a successful campaign for all digital marketers, as it enables accurate audience targeting, integrations and resolution. of identity. Leveraging ML is becoming increasingly vital to this effort as these technologies help marketers build personalized patient audiences, resulting in more relevant and timely advertisements and reducing the risk of consumers becoming burned out. by messages from a company. With increasing available inventory and competition, ML streamlines campaign analysis and optimization and improves audience quality.

Marketers need to make sure they select a DSP that can give them peace of mind that they are reaching their target audiences and not wasting valuable advertising dollars repeatedly on the wrong people. This is especially important as pharmaceutical marketers seek to expand their campaigns across various digital channels, such as Connected Television (CTV). This is where the value of first-party data shines through, as it allows marketers to make informed decisions and use vast information to support their overall targeting efforts.

Consumption of information is no longer a single channel and DSPs must have an element of versatility and omnichannel integration capabilities. This is crucial in the healthcare industry for patients seeking timely treatments and information on potentially life-saving drugs through advertisements from various sources. Real-world campaigns using this technology have a proven success rate linked to this effort, improving audience quality by 30%, as demonstrated by a DeepIntent customer case study, and helping to deliver relevant health insights of an individual. In a cookie-free future, omnichannel advertising will become especially difficult, given the challenges of cross-device and cross-channel identification. Your DSP must be well prepared for this.

Increased success through campaign optimization

ML is also of great value to marketers when it comes to campaign optimization – a crucial part of a DSP designed for a cookie-free future. Optimizing at the most granular level of a campaign requires real-time analysis and decision-making that takes into account millions of inputs and factors. Although it is impossible for a human to sort through this information manually, AI performs these tasks quickly and efficiently. ML algorithms automate decisions in real time, accelerating performance and reducing overall costs. Algorithmic optimizations happen weeks earlier than manual optimizations and continue for the duration of the campaign. Additionally, these algorithms are constantly improving based on fresh data, campaign performance results, and ongoing machine learning. All of this leads to better, faster and smarter campaigns.

Balancing data usage and privacy

Especially since third-party cookies are now a thing of the past, DSPs must prioritize practices that conceal the identity of ad recipients, while simultaneously using data that enables precise audience targeting. This is a task much easier said than done. When evaluating potential platforms, healthcare marketers should consider whether they have been designed to meet HIPAA de-identification standards that anonymize patient identifiers to prevent potential leaks of personal information. sensitive. Additionally, there are a variety of state and national online privacy laws that DSPs must comply with.

This does not mean, however, that the vast amount of data available to marketers should be set aside. It is estimated that around 30% of the world’s data volume is generated by the health sector. By 2025, the compound annual growth rate of data for healthcare will reach 36% – 6% faster than manufacturing, 10% faster than financial services and 11% faster than media and entertainment.

There is a balance to be struck between using data to improve business results and privacy. In fact, comprehensive, real-time data is the best tool a marketer can use to inform their approaches – and it’s crucial that the lead DSP has these capabilities. Data means nothing to marketers unless it’s updated frequently, because outdated numbers and information can hurt campaigns, leading to unnecessary spend and poorly targeted audiences.

Looking forward

DSPs have changed the game for programmatic advertising by making media buying more efficient, targeted and profitable. Healthcare advertising presents unique challenges as marketers struggle to strike the necessary balance between scale and precision. With the possible phasing out of third-party cookies, this changing environment becomes increasingly difficult to navigate, and the choice of a DSP built for the future with omnichannel integrations, strong privacy protections, and increased data processing capabilities. Data inventory and analysis has never been more important.