How Digital Advertisers Can Adapt to Privacy Changes With First-Party Data

Concept illustration of data personalization

By Will Melzer, VP Sales EMEA, Moloco

In July, Google stated that it would push back its phase-out of third-party cookies until the second half of 2024. This announcement comes off the back of several privacy changes in the sector, such as Apple’s removal of IDFA and Google’s implementation of the Privacy Sandbox. While marketers and advertisers can breathe a temporary sigh of relief, the impending demise of cookies will force many to reconsider their digital advertising strategies.

Though lauded for putting consumer privacy first, the removal of third-party cookies will transform the way ads are targeted, making it more difficult for advertisers to track and identify customers and their interactions with brands. With many now struggling to offer personalised experiences or retarget ads, the privacy changes have left marketers scrambling for alternatives that comply with a privacy-first approach. So, how can marketers best adapt to these changes?

These privacy changes don’t necessarily constitute the end of targeted advertising, it’s merely up to marketers to adapt to the changing digital landscape. Digital advertisers have access to a wealth of first-party data that can inform their advertising strategy, and this is the prime opportunity for brands to leverage it.

Enter: first-party data

Beyond the third-party data stream lies a trove of first-party data that allow marketers to gather accurate, timely, and relevant information in each step of the user journey without inhibiting privacy. In fact, Deloitte’s 2022 Global Marketing Trends report found that 61% of high-growth companies are adopting a first-party data strategy as they navigate the changing digital marketing landscape.

First-party data collected with consent is a readily available asset that marketers can use to fulfil a privacy-first advertising strategy. Those who invest in building and growing their first-party data sets can further map and attribute user engagements to business goals. By taking advantage of the types of machine learning and automation that Google and Apple have long championed as enablers of privacy and security, marketers can ensure that their ad campaigns remain laser-targeted to specific users who are most likely to engage.

Tapping into first-party data is crucial for marketers looking to retain existing users, reach new audiences, and drive performance. Further, personalised ads generated from first-party data can help users discover new products and brands, increasing their overall satisfaction and experience. But does a personalised experience outweigh user privacy?

The privacy debate

It is crucial for digital advertising to strike the right balance between privacy and personalisation. Personalised ads are the subject of much debate and digital advertisers are contending with a constantly swinging pendulum, between users who want personalised ads and those who feel like it’s too invasive of their privacy.

However, a McKinsey report found that 71% of consumers expect companies to deliver personalised interactions, while 76% get frustrated when this doesn’t happen. Additionally, the same research indicated that companies using personalisation generate 40% more revenue than average players. Advertisers are therefore faced with a dilemma – how can they continue offering hyper-personalised user experiences without compromising consumers’ right to privacy?

Although recent figures show that over 80% of marketers are concerned that the privacy-first era will affect their ability to optimise performance and personalise ad campaigns, many are moving towards first-party data strategies that bake privacy in right from the start. It’s important that the right balance is met for companies to be privacy-compliant without losing trust and sacrificing user preferences.

Powering first-party data with machine learning

Even with first-party data to hand, it remains difficult for marketers to adapt to new privacy changes. This is where automated machine learning comes in. Marketers monetising on their first-party data for targeted ad campaigns can further enhance datasets with automated processes that can rapidly and scalably adapt to change and constantly learn from first-party data in real time, while continuing to prioritise user consent and privacy.

Harnessing automated machine learning has several benefits. Those that use Deep Neural Networks (DNN) are able to immediately optimise using first-party data, driving faster digital growth and performance. Once these capabilities are in place, marketers can deliver targeted and tailored ad campaigns more effectively, meaning that ads will be seen by the right audiences at the right time.

There is no doubt that the future will be uncertain and challenging, as regulations shift to accommodate privacy. However, digital advertisers can rest easy knowing that they have an extended arsenal – they can rely on their first-party data and are armed with effective machine learning solutions to provide greater advertising efficiency at scale. These capabilities will enable digital advertisers to continue providing a truly personalised experience for their users.