With a cookieless future looming, marketers across all industries are being forced to rethink their current strategies and approaches. Perhaps most importantly, this new era in advertising will call for an increased focus on demand side platforms (DSPs) that can thrive in this new and evolving environment.
Since DSPs are significant investments – and play a pivotal role in the success or failure of campaigns – it’s vital that marketers understand exactly what they’re paying for, and how different DSPs compare to one another.
This will increase the urgency for some important considerations – one of which is determining whether your primary DSP is designed for success for the cookieless future, particularly regarding how it leverages machine learning (ML) for audience modeling, campaign optimization, and privacy. Further, the importance of first-party data and integrations cannot be overstated. As cookies phase out, this data is the key to successful audience targeting, which is the foundation of a successful campaign with meaningful results.
Leveraging Machine Learning (ML) for Effective Data Analysis and Audience Targeting
Collecting and utilizing first-party data is perhaps the most important component of a successful campaign for all digital marketers, as it allows for precise audience targeting, integrations, and identity resolution. Leveraging ML is becoming more and more vital to this effort, as these technologies help marketers create custom patient audiences, resulting in more relevant, timely advertisements and decreasing the risk of consumers becoming burnt out on a company’s messaging. With increasing available inventory and competition, ML streamlines campaign analysis, optimization, and improved audience quality.
Marketers need to ensure that they select a DSP that can provide them the peace of mind that they’re reaching their target audiences, and not wasting valuable ad dollars repeatedly on the incorrect people. This is especially important as pharma marketers look to extend their campaigns across various digital channels, such as connected TV (CTV). It is here where the value of first-party data shines through, as it opens the door for marketers to make informed decisions and utilize vast insights to support their overarching targeting efforts.
Information consumption is no longer one, singular track, and DSPs need to have an element of versatility and omnichannel integration capabilities. This is crucial in the healthcare industry for patients seeking out timely treatments and information about potentially life-saving drugs via advertisements from a variety of sources. Real-world campaigns leveraging this technology have a proven success rate related to this effort, improving audience quality by 30% as demonstrated in a DeepIntent client case study, and helping deliver pertinent, relevant information to an individual’s health. In a cookie-less future, omnichannel advertisement will become especially hard, given challenges of cross-device and cross-channel identification. Your DSP should be well prepared for it.
Increased Success With Campaign Optimization
ML is also of great value to marketers with regard to campaign optimization – a crucial component of a DSP built for the cookieless future. Optimization at the most granular level of a campaign requires real-time analysis and decisioning that takes millions of inputs and factors into account. While impossible for a human to sort through this information manually, AI conducts these tasks quickly and efficiently. ML algorithms automate real-time decisions, accelerating performance and reducing overall costs. Algorithmic optimizations happen weeks earlier than manual optimizations and continue throughout the duration of the campaign. Further, these algorithms are constantly self-improving based on refreshed data, campaign performance outcomes, and ongoing machine learning. All of this leads to better, faster, and smarter campaigns.
Striking a Balance Between Data Utilization and Privacy
Especially as third-party cookies become a thing of the past, DSPs must prioritize practices that conceal the identity of ad recipients, while simultaneously utilizing data that enables accurate audience targeting. This is a task far easier said than done. When evaluating potential platforms, healthcare marketers must determine whether it has been designed to meet HIPAA de-identification standards that anonymize patient identifiers in order to avert potential leaks of sensitive personal information. In addition, there are a variety of state and nation-wide online privacy laws that DSPs must comply with.
This does not mean, however, that the vast amount of data accessible to marketers should be cast to the side. It is estimated that approximately 30% of the world’s data volume is being generated by the healthcare industry. 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 data utilization to further business outcomes and privacy. In fact, comprehensive, real-time data is the greatest tool a marketer can use to inform their approaches – and it’s crucial that the primary DSP has these capabilities. Data means little to nothing to marketers unless it is frequently updated, as outdated numbers and insights can be detrimental to campaigns, resulting in wasteful spending and mistargeted audiences.
DSPs have changed the game for programmatic advertising by making media buying more efficient, targeted, and cost-effective. In healthcare, advertising presents unique challenges, as marketers strive to strike the necessary balance between scale and accuracy. With the eventual phasing out of third-party cookies, this shifting environment is becoming trickier to navigate, and choosing a DSP built for the future with omnichannel integrations, strong privacy protections, and increased data inventory and analytics capabilities have never been more important.