Mastering the new requirements for effective omnichannel marketing

Marketers who have spent more than a decade trying to bring omnichannel strategies to life are being confronted by harsh realities as we near the final quarter of 2023. On the one hand, evolving platform policies and privacy regulations are changing established data approaches and sources. At the same time, the rise and maturation of new advertising channels and opportunities continue to ask more of marketers and media planners.

Effective and efficient omnichannel marketing is still possible, but making simple tweaks to obsolete cookie-based strategies built on an outdated channel mix isn’t going to get brands and agencies where they need to be. Today, a strong omnichannel marketing approach needs to be built from the ground up based on a few new realities, including the use of privacy-compliant data, predictive modeling, machine learning and fast-growing channels like connected TV (CTV) and rideshare advertising.

A mobile-first lens for data-driven success

In the U.S., mobile media consumption continues to displace media time spent in traditional channels. In 2023, the number of U.S. adults using mobile phones will grow to 253.3 million, and those users will be spending the majority of their mobile device time with in-app experiences—an average of 3 hours, 28 minutes this year.

Given the personal nature of mobile devices and how they dominate consumers’ time spent with media, mobile represents the natural foundation on which to build a truly data-driven omnichannel marketing strategy. However, as mobile advertising IDs (MAIDs) vanish from the landscape, marketers need a new strategy for understanding consumers through a mobile lens.

As an example, T-Mobile Mobility Data gives marketers a comprehensive view of which apps people choose to download and how long they engage with those apps (or whether they do), all from a large, robust and representative panel.

App ownership and engagement data (i.e., T-Mobile Mobility Data) also represents a stronger indicator of consumer intent than standard web-browsing activity, since the apps people have on their devices, how they use them and for how long is the clearest representation of their interests and behaviors. These rich data sets can also be segmented into behavioral personas to enable marketers to target audiences based on interests, usage and intent.

The quality and richness of such data means that target groups can be very specific, and the tactics used to reach them can follow suit. T-Mobile Mobility Data is also privacy-compliant, since it doesn’t include sensitive information, and consumers can opt out of the use of their data.

Predictive modeling, machine learning and scale

The loss of addressability via third-party cookies and MAIDs continues to upend traditional advertising tactics. However, advances in machine learning and predictive modeling are stepping in to preserve the ability to customize messages to targeted audiences with precision, at scale and without the need for identifiers.

Predictive modeling using T-Mobile Mobility Data leverages a pool of permissioned user data to identify patterns and predict audience characteristics and behaviors, making it a privacy-compliant path to true addressability.

Machine learning and predictive modeling are essential components of modern omnichannel marketing. But not all audience solutions are created equal, as most are not being fed sufficient seed data that’s representative of the overall U.S. population. Here again, mobile data sources stand apart from the rest. T-Mobile Mobility Data, in particular, can be transformed via predictive modeling and machine learning into highly targeted advertising executions at a scale that few other platforms, if any, can offer.

The right channel bridges

While effective omnichannel marketing starts with a mobile lens, the opportunity to build bridges to other channels based on mobile insights is limitless. Today, effective omnichannel marketing requires strategic extensions to the many places consumers spend their media time, not just in-app and other established channels but also fast-growing spaces like CTV, digital out-of-home (DOOH) and rideshare advertising.

T-Mobile Mobility Data, and the behavioral and media consumption insights it unlocks, can provide the bridge to targeted omnichannel advertising opportunities. Marketers can leverage mobile insights to reach the most relevant audiences for their brands across all screens. Furthermore, they’re able to measure the ad effectiveness of these cross-channel digital campaigns and leverage in-depth brand insights for future media planning.

As marketers prepare their omnichannel strategies for 2024 and beyond, they’re hurrying to assemble new and reliable sources of scalable and privacy-compliant data. By starting with mobile engagement data and scaling insights to other channels via predictive modeling and machine learning, they don’t just bridge a gap. They open a new door.