Sandboxes and walled gardens: performance marketing with first-party data

The death of the cookie doesn’t need to mean the death of ad revenue for the mobile apps that live outside walled gardens.

Will Melzer, VP Sales EMEA, Moloco explains why the loss of cookies and personal identifiers has led to a greater emphasis on collecting first party data.

Despite Google’s delay in phasing out third-party cookies, there can be little doubt that marketers are needing to prepare for a world when much less third-party data is available to them to understand and reach their audiences.

This latest announcement from Google comes amidst a major overhaul for the digital advertising industry. From the removal of Apple’s IDFA to the pending cookie apocalypse, privacy changes are being gradually introduced to address consumer concerns around access to personal data.

The removal of third-party cookies will change how ads are targeted, with Google’s Privacy Sandbox set to come into effect as an alternative, less intrusive digital advertising solution – forcing marketers to rethink their advertising strategy. While it may seem like a grim prospect for digital advertising, these privacy changes don’t need to mean the death of targeted advertising.

Rather, it should be seen as an opportunity for digital advertisers to leverage their own first-party data to inform their strategy.

The privacy/personalisation pendulum

A notable obstacle in marketers’ search for a viable solution to third-party data is the privacy/personalisation pendulum of consumer sentiment. The pendulum has swung firmly towards privacy in recent years, with research from Adobe’s latest Trust Report finding over 75% of consumers are concerned with how companies are using their data, and many expect more transparency around privacy practices.

Google’s Privacy Sandbox reflects this sentiment and prioritises consumer privacy, making it more difficult for advertisers to identify and track user activity and interactions.

The new solution, however, will also have a marked impact on digital advertising practices such as ad targeting, remarketing, and personalisation – leaving 80% of marketers concerned about their ability to personalise advertising campaigns and optimise performance. Marketers are in a predicament.

How can they respect consumers’ right to privacy and still provide them with the personalised experiences they have grown accustomed to? Privacy-first marketing with first-party data Marketers need to find a balance and pivot their advertising strategy to reflect this privacy-first sentiment while earning the trust of users to provide the experiences they crave.

The solution lies in first-party data. It is important for marketers to look beyond the frothy third-party data stream and leverage the data from their own apps to enable an advertising strategy that respects user privacy. This, according to Deloitte’s 2022 Global Marketing Trends report, is how 61% of high-growth companies are navigating the changing digital marketing landscape. Adopting a first-party data strategy enables marketers to gather and analyse more accurate, timely, and relevant intel from each step in the user’s journey, without sacrificing privacy or relying on potentially incomplete, erroneous, or non-compliant data from third parties.

By tapping into the data they already have on-hand and investing in technology that delivers and acts on insights from first-party data, marketers can map and attribute user engagements to business goals and, by taking advantage of the types of machine learning and automation that Google and Apple have long championed as enablers of privacy and security, ensure their ad campaigns are laser-targeted and customised to the specific users most likely to click, act, and spend.

This approach is key, as personalised ads help users discover apps and other digital products, enhance their experiences, and increase their overall satisfaction. Automation and insights with machine learning It has become increasingly challenging for marketers to adapt to privacy changes.

This is where automated machine learning-based approaches have a distinct benefit – they adapt to policy and platform changes faster and more holistically than manual methods.

Not all machine learning-powered solutions are the same, so marketers need to ensure that the solution they implement uses deep neural networks to rapidly and scalably adapt to change and constantly learn from first-party data in real time. It's never been more crucial for digital advertisers to get the most out of their own data. By leveraging first-party insights and implementing machine learning solutions, marketers can ensure ads will be seen by the right audience at the right time in the right place.

These capabilities will set savvy performance marketers apart and lead to even greater advertising efficiency at scale as privacy and security challenges evolve, enabling them to continue to provide truly personalised experiences for their customers both now and in the future.

By Will Melzer