Apple’s privacy changes being stricter than expected reinforces the notion that predictive marketing intelligence is a must.
Clarification that Apple’s privacy changes will be stricter than expected is just the latest blow for digital publishers reliant on building strong audience relationships to drive their revenues. Google’s deadline on third-party cookie support and the Federal Court of Justice ruling that legitimate interest isn’t a valid purpose for ad tracking.
The fact that consent will be needed to trace users across iOS apps and browsers means cookie-generated data will fade at an even faster rate, along with the traditional mechanisms of profile building and message tailoring. Little wonder some industry pundits are arguing the wider technology ecosystem should start building arks to weather the ongoing deluge.
Yet, the problem with looking to the arks developed by tech giants is that it only increases publisher dependence on them and their data. As the industry seeks alternatives to cookies, there’s an opportunity for the open web to leverage its own assets.
Enter predictive marketing intelligence — the next frontier of efficient data monetization.
There's a reason why Google recently dialed up its predictive analytics capacity. In the post-cookie world where identifiers have less scope, marketers and publishers alike will need intelligent tools that enable them to make the most of accessible data. And as shown by the new updates to Google Analytics, the tech titan is keen to meet this need by offering AI-based solutions for first-party data processing and trend prediction – however, while retaining its power as the gatekeeper of data access and analytics.
To give businesses a true competitive advantage, data tech innovation must aim at breaking down the barriers to data access and understanding, while fostering data independence. As the industry strives to build a more transparent and fairer data supply chain, these transformative initiatives will be crucial to allow publishers to better understand audiences, but also use first-party information to efficiently build audience segments, in a privacy-compliant way.
While publishers recognize the huge potential of their rich audience information, it’s frequently too disparate and disorganized to activate. Because users interact with content in infinitely varied ways, data about their activities flows in an unstructured tide. The mixed audiences that sites attract — usually a blend of logged-in and anonymous users — also mean data is often incomplete; covering known attributes about some opted-in individuals and fragments of information about others.
The upshot, however, is that most publishers are left grappling with fragmented data that makes it difficult to produce the holistic individual-level view needed for accurate profiling; particularly if internal data capabilities are minimal. Fortunately, the new breed of marketing intelligence platforms that leverage predictive analytics presents an answer to many of these problems.
In addition to coordinating dis-jointed data, predictive analytics tools can help publishers turn it into a viable basis for accurate, impactful, and privacy-led marketing.
By deploying smart algorithms to analyze on-site activity, advanced analytics tools produce instant insights into user habits, needs, and interests — allowing publishers to augment individual profiles and create granular segments in real-time. As a result, they will be able to offer refined yet cookie-free audience segments fuelled by reliable quality data; a competitive advantage that’s especially important when 35% of marketers are losing vital budget to inaccurate targeting.
Moreover, using persistent analytics that draws on incoming and historic data can also anticipate what users are likely to be interested in next —in terms of content and brand messaging — giving publishers the means to optimize the value of their assets on all sides.
Publishers can even combine known user characteristics with smart modeling techniques to learn more about their overall audience, anonymous users included. Where previous lookalike models have typically required cumbersome data processes, often taking weeks to run — not to mention relying on tech heavyweight’s "black boxes"– emerging platforms are capable of real-time, streamlined, and transparent automation.
Going a step further, equipped with powerful insight gleaned from their first-party data, publishers can further extend audience knowledge by exploring data enrichment and exchange solutions in partnership with marketers. With a clearer picture of complete audience trends and preferences, publishers will then have the means to create even more tailored content and bring 100% of their users within the targeting range.
Publishers already have the key ingredient needed to keep the digital marketing system operating and maintain crucial revenue; they just need to apply it more effectively. With the progression of predictive marketing, analytics has come to a wider world of first-party data possibilities. For smart publishers, the next best move will be to embrace the new frontier of data monetization with advanced marketing intelligence and stronger data independence.
(***This article also appeared on Dataconomy.)