By Jürgen Galler
Predictive analytics is bringing smarter insights and better efficiency into many areas of our lives, even if we aren’t always aware of it. Take healthcare, for example, a sector that has been firmly in the spotlight in recent months. Scientists have recently combined self-reported symptoms data and artificially intelligent (AI) modeling to predict which early signs of COVID-19 can be used for faster detection.
Interest in using data stores to identify useful patterns is also sparking growing commercial interest. Companies that have watched leading players such as Amazon harness AI engines to fuel ever-more refined product suggestions are starting to see these advanced tools as a potential solution to many issues. That includes not only tackling ongoing struggles with delivering personalized digital experiences but also doing so without third-party cookies.
Data privacy has been climbing up the business agenda for years, with regulations and restrictions constantly bringing fresh customer connection challenges. As access to third-party cookies fades, predictive technologies offer an opportunity to use first-party data as the basis for building marketing and advertising strategies that operate effectively in a privacy-centric world.
Going cookie-free means doing more with less
Companies have spent 20 years tapping real-time data signals from third-party providers to find, match, and reach relevant audiences with targeted digital advertising. So, it follows that reconfiguring this long-running system is a huge undertaking — one even Google looks to have underestimated — which will mean major changes for traditional data practices.
Among the biggest shifts will be adapting to lower data availability. Recognizing the need to create new data frameworks, organizations are turning their gaze towards alternative, direct sources – with over half of global ad execs set to increase the use of first-party data. But while this approach has many benefits, including continued access to privacy-safe insight, it’s also likely data volumes will fall as the amount of information shared by users varies.
Ensuring future success, therefore, depends on achieving more with less. Google, for example, has already shown one way that predictive tools can optimize existing assets. Drawing on anonymized browsing data, its Federated Learning of Cohorts (FloC) proposal applies AI modeling to sort users into targetable segments in line with specific interests. By leveraging these types of predictive technologies, firms can maximize their own first-party data value without necessarily strengthening their ties to walled gardens.
Predicting a path to better targeting
As wider data management solutions develop AI capabilities, there is growing potential for businesses to make better use of the data they own.
For starters, AI-supported processing powers instant consolidation and analysis of diverse data sets. Providing a granular view of unique needs, tastes, and preferences for consenting users, these smart developments allow accurate profiling and highly tailored experiences.
The capacity to blend that data with predictive analytics, however, enables companies to go further. By using machine learning algorithms to delve through large pools of data and uncover behavioral patterns, advanced platforms can predict how users will act next and what they’ll want. Plugged into ad servers or exchanges, this insight drives the delivery of impactful in-the moment ads, boosting the chances of sales, customer satisfaction, and loyalty. When implemented alongside smart performance measurement, it can create a constantly replenishing loop – with data about what hits the right note used to continually improve precision.
And the applications don’t end there. For those aiming to expand the scalability of first-party data, AI can also be invaluable to extend targeting options and scope.
Creating a workable range of alternatives
In the long-term, predictive technologies are poised to enhance the viability of cookie-less ads on two key fronts. Firstly, they give businesses the means to enrich user-level data in a privacy-friendly way by working from predicted, not declared, attributes. Running predictive modeling and pattern assessment of “ground truths” about known users can reveal specific traits, allowing businesses to identify lookalike audiences across the web and narrow targeting by category: be that age, gender, or interest in purchasing particular products.
Secondly, it can super-charge contextual advertising. Mostly used on the publisher side, evolution in AI analysis of user content consumption significantly enhances targeting for companies. With sharper, interest-centric segmentation, they’ll be able to align messages with greater accuracy than was ever possible with broad keywords, and these possibilities will also allow them to easily switch how ads are tailored, depending on available data.
Reliable future-gazing is a tantalizing prospect for almost every industry. With a clearer idea of what’s on the horizon, organizations can both effectively prepare for upcoming changes and proactively capitalize on developing opportunities. As AI integration in everyday business grows, that vision is looking more and more like an imminent reality. The companies that start using predictive capabilities to hone their targeting strategies now will be the ones to benefit from transformed customer connections, as well as assured post-cookie success.
About the author
Jürgen Galler is the CEO & Co-founder of 1plusX. Due to his remarkable career in international management, Jürgen has earned a solid reputation as an expert in digital business. He spent three years in Japan in IT consulting and then returned to Europe to develop the digital offering of the media conglomerate Bertelsmann. In 2007 he joined Google where he led the development of Google Search, Chrome and of the YouTube EMEA products. After Google, he became the strategic mind behind Swisscom, Switzerland’s leading telecommunications company, for two years. In 2014 he founded 1plusX combining experience and passion for digital marketing, machine learning and artificial intelligence.