The foundation of traditional advertising has long rested on the shoulders of big offline third-party data providers. Direct mail campaigns, ranging from credit card offers to home repair services, have used deterministic data for several decades. For example, a window replacement company might leverage local real estate transaction records, like mortgages, to gather information about homes in their area. This could include square footage, heating method and the year the home was built. By analyzing this publicly available data, the company could pinpoint members of its target audience, narrowing its audience down from the entire local community to individuals whose needs align best with their services.

Through the mortgage records, the company can identify homes with larger square footage, indicating more windows and a potential need for more window replacements. With new windows typically lasting 15 to 20 years before requiring replacement, the window replacement company can also strategically target homes within and beyond this building timeframe. Moreover, they can focus on homeowners who might be more concerned about energy efficiency, such as those using electric heating. This has been the standard practice by companies, both big and small, for many years.

However, online advertising was a paradigm shift in how target audiences were and are isolated. Marketers developed pixel-based solutions where they tracked what people were browsing online and inferred demographic information about them based on this activity. To continue the window repair example, advertisers might track who is browsing HGTV or HomeAdvisor in the company’s local area to determine who might be in the market for new windows. For certain products and services, this browsing intent information is helpful but it is ultimately based on guesses and can lack cohesion with the rest of the brand’s targeting strategy. Aligning online and physical advertising audiences is important for addressing the gaps with each. The goal is not just to accurately target, but to create a multitouch outreach to consumers across different platforms.

With Apple already blocking third-party cookies and Google discontinuing cookies for 30 million Chrome users in January, the enduring significance of quality public information for precision targeting is being proven in 2024. Unlike third-party cookies, which belong to an adtech platform, this data is from primary government sources and self-reported, proving to be remarkably accurate. Information sourced from voter registration, home purchases and driver’s license details possesses a level of reliability that probabilistic online data struggles to match. Amid ongoing industry disruption, these identifiers remain constant, serving as a dependable data source for marketers.

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Simultaneously, brands are placing a heightened focus on first-party data. Belonging to website owners and acquired through customer consent, it is a prized asset for brands. It remembers what the user does in their domain session, their username and what items they added to their cart. For brands, first-party data is more than just compliance with the growing state privacy regulations, it is how they understand customers on a personal level. Unlike the generalized assumptions of probabilistic online data, first-party data empowers brands with real knowledge of customer preferences, behaviors and interactions. For example, while one might assume that all Starbucks coffee drinkers share similar characteristics, the brand, armed with its first-party data, can discern nuanced groups among its customer base, from those who drink coffee every morning to those looking to avoid the occasional afternoon slump.

Bringing this first-party data into the digital realm is a risk that requires the involvement of reputable, trustworthy onboarders. Transparency must take center stage as brands entrust the partners with customers’ personal information. Scrutinizing storage capabilities, understanding match keys and evaluating the onboarding process is imperative.

A reputable onboarder acts as a custodian of sensitive customer information, maintaining clean ethical practices and secure storage capabilities. Evaluating an onboarder’s approach to data matching is crucial, as it reflects their effectiveness at turning raw offline data into digital IDs usable for digital advertising. Brands need to question not only the match rate but also the financial value. For instance, if an onboarder can only engage with 30,000 out of 100,000 potential customers, the brand should assess whether this engagement covers a large portion of its target audience to garner strong enough returns to make the investment worthwhile.

Beyond just match rates, savvy brands should demand to know who they are reaching and, just as importantly, who they are not. This allows for segmented follow-up outreach. The subset of the audience that remains untouched through digital means should be reached through alternative channels, such as by phone or mail, for maximum results.

For example, during the student search season, prospective students and their parents were targeted by a higher education institution with ads that encouraged them to take campus tours and complete applications. The emphasis here was on reach, to engage as many high school seniors and parents as possible. However, in the yield season, after applications were received and students were accepted, the challenge shifted to closing the enrollment process with the specific group of students offered admission. The match rate and reach become pivotal metrics in ensuring that the university’s digital advertising is reaching its prospective students effectively to convince them to pick their school.

This specific university had an 82% match rate with its 200,000 prospective applicants during the student search season. For the 18% of prospects not successfully matched, a tailored approach using postcard communications was deployed. This adaptable, multi-channel audience reach was key to the university’s success. This proved to be not only effective but also cost-efficient, particularly when compared to the traditional, expensive methods of physical mailers to all prospects.

Navigating today’s ever-evolving advertising landscape requires a strategic blend of traditional targeting methods and innovative tech-driven solutions. Brands that master this blend will reach audiences more effectively and adapt campaigns more cost-effectively.

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Missed The Latest Episode of The SalesStar Podcast? Have a quick listen here!

Episode- 193: A Chat on AI with Debbie Braney, Vice President, Demand & Brand Marketing at Glassbox

Episode 192: The Latest in Distributed Commerce with Byron Sorrells, CEO and Co-founder of Dispatch

Episode 191: The Future of Online Shopping with Anh Vu-Lieberman, VP of Conversion Rate and Optimization at Nogin