In today’s dynamic digital landscape, advertisers face the ongoing challenge of translating branding efforts into measurable returns. Junny Yiu, Senior Business Director at DoubleVerify, and Louis Ng, Head of Programmatic at Havas Media Hong Kong, recently shared their insights in an interview with Marketing in Asia. They discussed how brands can leverage advanced metrics and AI to bridge the gap between branding and ROI. By focusing on attention metrics and utilizing real-time AI optimizations, brands can gain actionable insights that drive both brand awareness and conversion, even within complex, multi-channel campaigns.
What are the challenges of linking branding efforts to tangible ROI metrics?
Junny Yiu: One of the biggest challenges in linking branding efforts to ROI is that branding is perceived to influence long-term consumer behavior rather than delivering immediate, measurable outcomes. Therefore unlike direct response campaigns where clicks or conversions provide clear KPIs, the impact of branding on consumer perception or recall is harder to quantify. Additionally, branding campaigns can result in increased website traffic or higher engagement on other channels, however this is not immediately measureable, thus making attribution complex.
Louis Ng: In digital advertising, branding and ROI measurement are fundamentally different. Branding measurement typically involves assessing how well consumers recognize or recall a brand through surveys, social media mentions, and search volume data etc. A common method for measuring the effectiveness of branding efforts is the Brand Lift Study, which measures the incremental increase in key metrics such as Ad Recall, Brand Favorability, and Purchase Intent.
On the other hand, ROI measurement focuses on lower funnel metrics like ROAS (Return on Ad Spend), revenue, and conversion attribution to evaluate the effectiveness or profitability of an advertising investment. By leveraging these metrics, it helps to optimize campaign strategies to achieve better lower-funnel outcomes. However, branding efforts often generate long-term value that may take months or even years to materialize. This makes ROI attribution more complex, particularly when multiple channels are involved.
How can tracking attention metrics improve the effectiveness of branding campaigns?
Junny Yiu: Attention metrics can help bridge the gap in attributing ROI to branding efforts by allowing advertisers to assess how effectively their campaigns are capturing and holding user attention. This serves as a proxy for future actions like brand consideration or purchase and also creates a foundation to identify opportunities for improvement in campaign performance. Attention metrics give advertisers a more granular view of how viewers engage with ads, which is critical for branding campaigns. Traditional metrics, such as viewability, tell you whether an ad was in-frame, but do not reveal if it actually engaged the intended consumers. Attention metrics, on the other hand, assess elements like time spent, interactions, and ad placement, providing a richer picture of consumer engagement. By optimizing for attention, advertisers can ensure their branding message is not only being seen but also resonating more effectively, leading to better recall, improved sentiment, and eventual conversions.
As highlighted in DoubleVerify’s Global Insights Report 2024, while retail sites and apps exhibit lower viewability, broader attention metrics reveal a greater influence on engagement. Significantly, 58% of APAC marketers intend to prioritize attention-based metrics for most of their purchases in 2024. Reflecting this shift, DV’s attention solution, DV Authentic Attention®, has seen a threefold increase in adoption over the past year.
Louis Ng: Attention metrics can significantly enhance the planning and optimization process by providing deeper insights into ad exposure. These metrics quantify the intensity and prominence of ads through measures such as viewable time, share of screen, video presentation, and audibility. Additionally, ad engagement analyzes key user-initiated events that occur while the ad creative is displayed, including user touches, screen orientation, video playback, and audio control interactions.
How do you see user engagement correlate with direct response KPIs like conversions?
Junny Yiu: Campaigns that capture greater user attention tend to see higher engagement rates, with users more likely to take a desired action post-exposure. Emerging AI tools are equipping marketers with useful strategies such as dynamic algorithmic activation and delivering sharper consumer insights. This enables real-time campaign optimization toward maximizing both engagement and cost efficiency which directly contributes to improved conversions. There is also a cross-channel impact that becomes evident here as engaged users, particularly when ads are attention-optimized. This means that ads that secure higher attention scores often drive increased click-through rates, leads, and sales.
Louis Ng: Recently, Havas launched a case study with DV on driving lower-funnel outcomes with actionable attention metrics. By leveraging attention metrics, granular insights can be obtained by media partner, targeting segment, creative format, and inventory list. These insights inform in-flight campaign optimization, resulting in a high overall attention index that surpassed the DV benchmark for comparable advertisers. DV found that lower-funnel outcomes correlate with the engagement index, a key element of attention. Armed with in-flight insights, Havas improved the engagement index by 58%.
Additionally, Havas conducted an A/B test to analyze results. The group with the DV Universal Attention Segment applied achieved an 83% higher click-through rate and a 46% lower CPC. Applying the Universal Attention Segment enabled Havas to take optimizations a step further and harness attention metrics to drive campaign KPIs.
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How can AI be leveraged to optimize campaign performance in real-time without sacrificing scale?
Junny Yiu: AI plays a critical role in optimizing campaign performance given its ability to analyze vast amounts of data in real-time. AI-driven tools can for example take into account attention metrics, consumer behavior patterns, and contextual data to make immediate campaign adjustments, ensuring ads are shown to the right consumers at the right time. With the always-on optimization enabled by AI, advertisers can maximize campaign efficiency without compromising reach. By using AI, advertisers can balance scale with quality and precision, ensuring that campaigns are both broad enough to reach a large base and precise enough to engage those most likely to convert. The ways in which brands activate their campaigns today is evolving as they find themselves in a constant loop of activation and optimization. AI is proving helpful here as it helps manage both scale and complexity simultaneously and in real-time. Scibids AI, our AI optimization tool, is a perfect example of this and is, thus, able to link actual media KPI achievements with tangible business outcomes.
Louis Ng: Today, AI can significantly enhance campaign optimization by leveraging real-time machine learning for budget allocation and bid adjustments towards the best-performing ad inventories. In the past, advertisers often relied on test-and-learn approaches and complex audience segmentation to optimize campaign performance, sacrificing campaign scale in the process. By leveraging predictive analytics and modeling via AI, advertisers can foresee the performance of ad campaigns based on historical data and real-time trends, allowing for more effective budget allocation, optimized bidding strategies, and identification of potential audiences likely to achieve campaign goals, without hurting campaign scale.
How can these optimizations be used to refine future campaign strategies?
Junny Yiu: The insights gained from AI-driven optimizations and attention metrics do not just improve current campaigns, they also inform future strategies. By understanding which elements of a campaign generated the most attention and led to conversions, advertisers can refine their creative and placement strategies for future initiatives. AI-driven optimization provides substantial advantages in safeguarding media investments and boosting campaign outcomes, while automation streamlines and reduces time spent on manual tasks. AI-driven campaigns not only save time but also deliver impressive returns. For instance, our Scibids AI technology can deliver an average ROI of 4 for every campaign dollar invested in it. These efficiencies extend from media buying to creative development, enabling brands to tailor content more effectively to their intended viewers. This heightened level of personalization and optimization allows for smarter planning, better resource allocation, and more effective campaigns over time, keeping brands agile in responding to shifting consumer behaviors and market conditions.
Louis Ng: The data and insights gathered from optimized campaigns provide valuable learnings, helping us understand what worked well and what didn’t, leading to continuous improvement in future campaigns. For instance, AI-driven lookalike modeling enables us to analyze existing 1st party data, including demographics, behaviors, purchase history, and engagement metrics. With these insights, advertisers can refine future campaign strategies to target new potential audiences, ensuring our marketing efforts are directed towards the most promising prospects.
As brands increasingly seek to link branding efforts with tangible outcomes, attention metrics and AI have emerged as vital tools in campaign optimization. Junny Yiu and Louis Ng’s insights underscore the importance of not only monitoring consumer engagement but also using advanced, data-driven strategies to enhance campaign effectiveness. By combining AI with attention metrics, brands can achieve lasting impact, balancing reach with precision, and setting the foundation for data-rich, results-oriented marketing strategies that resonate with modern consumers.
