Ran Milo, CMO of Bidalgo, explains how marketers can achieve granular optimization without user-level data.

User-level data is dying – first on iOS, but eventually on Android and other platforms as well. Its not the end of performance tracking and campaign optimization, though. Theres a better way to optimize user acquisition than with user identifiers. Its better for numerous reasons, from low privacy exposure to vast improvement potential. And unlike user identifiers, its not dying.

Yes, “dying” is the right word for whats happening, and we should accept this. User identifiers are about to go the way of the dodo for several reasons. Chief among them is that people worldwide are more privacy-minded than ever, mindfulness which translates into distrust of technology companies. This sentiment opens a window for populist data regulation and commercial stances designed to attract privacy-first customers. The result is a perfect storm of politics, marketing, and genuine outrage.

The latest uproar of this kind, over Apples ATT framework, is hardly unexpected or unwarranted. IDFA identifiers, the proverbial cheese thats being moved, are the cornerstone of user acquisition on iOS. Without permission to track, marketers cannot accurately trace the journey from ad view to churn. In other words, theres no longer a deterministic link between ads and the revenue generated by the people who saw them.

So, where do we go from here?
As marketers, we are taught to embrace data, rely on it, hoard it and tinker with it endlessly. The insights we glean are our competitive advantage in the crowded digital marketing ecosystem. Whenever someone deprives us of data points we find helpful, be it via regulation or a technological shift, the industry goes through what looks quite similar to the five stages of grief.

Four of these stages are reflected right now all over the internet. Some marketers are in denial, claiming that the change isnt meaningful. Then there are the angry ones, shouting at Apple. Others bargain by trying to find a loophole in Apples guidelines (no, fingerprinting is not OK).

With iOS 14.4 ad inventory dwindling and more restrictions on user-level data introduced across the industry, these grief processes will accelerate.

After everyone is through the final stage, acceptance, the mobile ecosystem will have no choice but to accept the coming changes and adapt. Even Facebook, which fought tooth and nail against Apples changes, has already laid out a new campaign structure for advertisers to follow.

But what does adapting to the new landscape look like? These changes impact everyone, from startups to large-scale app publishers, across genres, from gaming and entertainment to eCommerce, finance, productivity, and more. It will be much harder for app publishers to advertise if confidence in effectively measuring their results is gone. App publishers will have to reinvent and reeducate how they buy media and measure the efficacy of media spend.

Additionally, the changes have given marketers pause to consider their marketing mix. Many find themselves primarily using the major platforms (Google, Amazon, Facebook, and Apple) to acquire users (nearly two-thirds of total US digital ad spending in 2020 per eMarketer). This brings both risk of overdependence on any one platform and the ability to target audiences effectively, as well as ROI. As marketers diversify their marketing mix cross-platform/channel, the data sources are increasingly fragmented, making it more and more challenging to understand the complete performance picture.

Light at the end of the tunnel
Given that there are now fewer areas advertisers can control, due to innovations such as automated bidding and audience targeting, more marketing resources are freed up to focus on the third pillar of marketing – the ad creative customers see. Creative assets, which remain squarely within marketers control, are now far more important than ever before.

To make the most of their creative, marketers should realize that they are already sitting on a goldmine of creative-level data from their campaigns. That said, most of us are yet to fully embrace the benefits this data can bring when analyzed properly. With Apples IDFA and ATT changes, now is the time for the marketing industry to tap into this data and unlock its potential.

Using creative-level data, marketers can realign how they measure success and optimize for it around the one element they still have complete control over, the creative asset. This privacy-forward way of performance analysis relies on aggregated data and is a viable alternative for the disappearance of user identifiers.

Instead of looking at the value of each user, creative-level data enables marketers to look at the value of each creative asset across all the campaigns its in. This is done while using all of the familiar metrics and KPIs from either upper or lower funnel: CTR, CR, CPI, etc.

Furthermore, creative-level data can be used in ways that user identifiers werent suited for. For example, when looking at ad creative, we can use knowledge of past performance to model the potential for future success.

Its also possible to discover previously-unseen risks and opportunities, such as assets with an abnormally large percentage of spend tied to them or quality assets that dont get assigned enough budget.

How to approach creative-based data models
Leveraging creative-level data is all about building up and implementing a structured creative strategy. The sooner you begin, the better off youll be in the long run. That is because it takes time to collect enough data for actionable insights.

Firstly, you should always have strong naming or labeling conventions tied to each of your assets. While some metrics are asset-specific, many of the most valuable insights are dependent on an intelligent grouping of your assets. If each asset has an entirely different name in every ad its in, you will never be able to look at its performance across all ads which utilize it.

Secondly, you should understand how to define success and failure. The KPIs might be similar to those of user-level analysis, but the benchmarks will be different. In some cases, youll need to change KPIs altogether to establish proxy metrics to your real goals. Understanding this takes time.

When youre confident that you have mostly regained the level of visibility you had with user identifiers, its time to go deeper. Can your assets be used as a segmentation tool, attracting the right users while being less effective for others? Can you detect creative fatigue before it hits? Should you adjust your creative production to fit better with how your assets are performing? These are all questions you can answer with creative-level data, and theyre just the tip of the creative iceberg.

Its not all gloom and doom
Apple is right to want users to have more transparency in the data being collected and how its being used, but the rollout of ATT and IDFA changes has been fraught with issues. From platforms responding reactively to marketer confusion on how the changes impact their ability to target ads and how to pivot away from IDFA-based tracking, its been a challenging past 11 months.

But, as the dust has settled, solutions emerged that provide a path forward. Creative-level data has proven effective at offering campaign performance metrics, and its available regardless of company size – so SMB app publishers and large-scale advertisers can tap into creative-level data to optimize campaigns. Were optimistic about this new path forward for the mobile marketing ecosystem and looking forward to seeing how the industry readjusts with this new data source powering it.