Yieldmo

Yieldmo is an advertising technology company that operates a smart exchange that differentiates and enhances the value of ad inventory for buyers and sellers.

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It’s time for data-addicted marketers to reconnect with human beings

October 11, 2022

By Jeremy Steinberg, CRO & GM, Exchange, Yieldmo

Remembering that audiences are humans, not data points, is now mission critical for marketers. Before regulations like GDPR drastically accelerated the privacy clampdown, relentless profiling and tracking had become the norm in digital advertising. Restrictive changes by tech giants have further highlighted marketers’ laser focus on data points as they strive to understand audiences in a privacy-constrained world. Still, the consequences of this are clear: more than two-fifths of online consumers describe brands’ use of their data in advertising as invasive, and many are taking steps to block, skip, or avoid ads.

Creating a new, privacy-focused marketing funnel is essential for rebuilding brands’ all-important connections with consumers. To do this, marketers can now use evolved contextual targeting methods and behavioral insights, alongside trustworthy data partners and first-party data, in non-invasive ways to deliver relevant advertising without inappropriate personalization. But is the industry ready to embrace new approaches?

Targeting needs to be redefined for the post-cookie era

With the delays in phasing out the third-party cookie, some marketers may be reluctant to try new ways of targeting. And among those actively looking to replace third-party cookies, the focus so far appears to be on technologies — such as fingerprinting and IDs — that offer similar functionality to cookies but aren’t future-proofed in terms of privacy concerns. Though this approach is viable, it can only scale so far, especially when the privacy landscape is far from static.

If in the future regulators deem these types of ID solutions invasive, for instance, the businesses investing in them now will once again be scrambling for a viable replacement. The ecosystem is leaning in this direction because consumer expectations have seen marketers stuck between a rock and a hard place: more than 7 in ten consumers want personalization, even though many don’t feel comfortable sharing their data for marketing purposes. Research shows that the less ads rely on personal information, the more receptive consumers are to them.

All ad personalization derived from audience profiling and user IDs — no matter how sophisticated or privacy-compliant — has the potential to backfire if audiences believe marketers are overstepping personal boundaries. Urban Outfitters, for example, realized as much after trialing gender-based personalization on its website. Using this information to target its online customers with “relevant” apparel fueled many complaints, particularly from women who frequently purchased menswear and felt alienated as a result of the targeting.

To ensure audiences genuinely connect with brand messages and that targeting is privacy-complaint, marketers must shift away from this kind of personalization. By using updated contextual and behavioral signals, or only the most trusted sources such as companies like Experian, they can transform targeting to focus instead on relevance and receptivity.

It’s not enough to get the right message to the right person

Contextual targeting today goes beyond assuming consumers want to purchase a car just because they land on an automotive brand’s website. While this assumption may be correct, the wider context around this moment can impact how receptive someone is to a brand message. For instance, a consumer may be less likely to conduct a high consideration purchase if it is the middle of a workday.

Marketers must be in touch with audience receptivity to understand when consumers will engage with relevant messages and when they will be irritated by them. This can also vary depending on the environment of the ad. Deloitte Insights uncovered that Gen Z and Millennial consumers find social media and influencer ads memorable rather than annoying, while, to varying degrees, all generations disliked ads appearing during the streaming of video, games, or music.

Machine learning (ML) and artificial intelligence (AI) technologies now enable marketers to account for all of these variables in real-time. These solutions can process vast quantities of data and accurately predict how individuals will respond to brand messages, using signals that marketers didn’t have access to during the early days of contextual. This means marketers can select the optimal placement, ad format, and ad elements for a particular webpage to reach and connect with consumers at the most effective cost. Smart algorithms can deliver advanced contextual signals for better performance on both the buy and sell side.

By continuously evaluating campaign performance and introducing more up-to-date signals into the decisioning process, ML and AI-powered tools allow marketers to take a more nuanced, impactful, and human approach to targeting.

How context can unlock a greater understanding of complex human behaviors

While there are companies that provide privacy-forward data and insights, which can be valuable for secure targeting, contextual targeting enables marketers to reduce their reliance on audience segments built using personal information. Additionally, audience segmentation doesn’t necessarily provide a full view of essential factors such as purchase intent and brand perception. With contextual, marketers can gain further valuable insights from how audiences interact with online content, as opposed to who audiences are.

Marketers can determine their attention level based on their interactions when consumers browse the web, tune into video content, or open an app. This piece of the contextual puzzle lets marketers optimize their ads and make them relevant to how a user consumes digital content. Do they usually mute online videos, for example, and watch with subtitles on? If so, then marketers know to prioritize the visual impact of their creative. Aligning ads with behaviors and not personal details then forms the basis of a respectful, privacy-first connection that adds genuine value to users’ online experiences.

Rather than simplifying consumers into a collection of data points, sophisticated contextual targeting uses AI and ML technology to help marketers interpret the full complexity of their audiences and translate behavioral insights into human connections. This then allows ads to be tailored in real-time to maximize both relevance and receptivity. Moreover, they can do so without using private information, thereby respecting user privacy and removing the risk of inappropriate, and off-putting, personalization.

Tags

Artificial Intelligence
Machine learning
contextual targeting
privacy