Brand Marketing: Focus on Outcomes and Cut Through the AI Hype

Google’s decision to call off its deprecation of the third-party cookie may seem like a reprieve for many brand marketers. But the truth is, regardless of this move, the digital landscape is increasingly privacy-centric.

As users take control of their data-privacy, brand marketers have two options: spend more or spend smarter. AI can deliver the second, but how to cut through the noise?

The Rise of Outcome-Based Brand Marketing

In a post-pandemic world, there has been a move towards outcome-based marketing (OBM). This approach shifts the definition of success from metrics like views and clicks to business-focused results, such as increased brand awareness.

OBM’s attraction is that it ensures marketing efforts genuinely impact a business’s bottom line. However, the approach is becoming difficult in a world that increasingly favors user privacy.

Technology companies are either phasing out identifiers such as device IDs and third-party cookies, or giving consumers greater choice over whether they accept them. This in turn makes it more challenging to link impressions to outcomes.

Without a powerful way to target consumers or measure impact, OBM risks becoming an inefficient numbers game: spending more money to reach the same number of people.

Countless AdTech tools promise to solve this problem with Artificial Intelligence. But how do you choose the right solution?

How Can AI Help?

The launch of ChatGPT propelled AI to mainstream consciousness in November 2022. Though many AdTech companies have used AI for years, ‘Chat’ showcased the technology’s ability to analyze, learn from, and act on complex data sets.

In an increasingly privacy-centric world, AI lets advertisers sweat the data they CAN access while still respecting user privacy. This might include location, contextual data, device type, time of day, and feedback from live user surveys.

AI algorithms can simultaneously consider thousands of such variables against historical performance models to achieve powerful, cookie-free targeting, measurement, and optimization.

• Audience segmentation and personalization – AI can analyze vast datasets to create granular audience segments in real time, based on behavior, interests, demographics, and more. This lets marketers deliver more personalized ads, improving relevance, engagement, and return on advertising spend.
• Predictive analytics: AI models can predict user behavior, such as the likelihood of downloading an app or buying a product. Predicative audiences are a more powerful version of lookalike audiences (which AI can also create). They allow brand marketers to allocate resources efficiently by focusing on high-potential audiences.
• Contextual and multivariate advertising – Without the need for third-party cookies, AI identifies relevant contextual signals, like keywords, to ensure ads appear next to relevant content – think an advert for a cooking sauce on a recipe app. It can also analyze the combined impact of multiple variables at once to predict and optimize performance.
• Creative optimization – AI can analyze ad performance and recommend creative variations in real time. For example, an AI-powered system might serve a discount voucher to an audience group on the cusp of buying a product or an influencer video to a consumer who is new to their brand.
• Attribution modeling – AI-driven AdTech can help marketers understand which channels have contributed to a particular outcome by untangling the complexity of multiple channels and touchpoints. It can ‘close the loop’ on ad measurement, by showing marketers the impact of upper funnel activity, such as brand awareness campaigns.

Choosing the Right AI AdTech Partner

With so many vendors touting AI-enabled solutions, brand marketers need to cut through the noise to find platforms that will lead to the best ad delivery decision.

Finding a partner with strong AI credentials is a challenge. The technology has been part of everyday life for some years – think Alexa, social media algorithms, or streaming recommendations. But it has been part of technology development for even longer, meaning most companies can legitimately claim to use some sort of AI. It is worth investigating in what form and to what extent. Can it, for example, do the tasks listed above?

The more pertinent question, however, is whether AI can help you deliver your desired marketing outcomes, whether you are aiming for increased sales, brand lift, or customer engagement. A trusted partner will be able to prove this with data and testimonials.

Scare stories about AI going wrong or claiming human jobs are the perfect excuse to delay adoption. However, as those who have not started testing privacy-focused solutions will already learn, this will only result in competitive disadvantage in the long-term.

It is time to embrace this exciting new era of both privacy-centric advertising and AI-powered targeting that promises to make programmatic advertising more efficient, relevant, and respectful of user privacy.