The Revolutionary Impact of AI on Conversions and Scale in Remarketing Strategies
The Paradigm Shift: From Broad to Precision Targeting
Over the last couple of years, many have leaned towards broad or “optimized” targeting via Google (P-Max) and Meta (ASC) for their remarketing efforts. This approach often stems from a lack of awareness of more effective alternatives.
However, the digital marketing landscape has evolved significantly, thanks in large part to advancements in AI technology. Even in a cookieless world, AI-driven solutions are proving to be not just viable but superior.
Case Study: AI Hyper-Segments vs. Rule-Based Segments
Kahoona's AI Predictive Segmentation model exemplifies this new era of remarketing. With 15X more data points than existing solutions, the variety of predictive segments at your disposal becomes robust.
With predictive segments such as Kahoona’s High Probability of Purchase (High PoP), intent segments, pricing level and loyalty segments, attitudinal segments (e.g., Impulsive users), psychographics (e.g., dark leather pant lovers), demographic segments (e.g., Female Gen-Z), and much more. Unlike traditional methods, Kahoona’s AI-driven approach has shown remarkable results, challenging previous benchmarks with its effectiveness.
- CVR and Scale Performance: Kahoona’s High PoP model achieved a 15% success rate in CVR, outperforming conventional strategies like Cart Abandonment-Based Remarketing (11%) and Cart Page Visit-Based Remarketing (13%). Moreover, its scale reached an impressive 89%, significantly higher than the 28% and 39% achieved by the traditional methods, respectively.
- Strategic Impact: The AI Predictive approach, specifically Kahoona’s High PoP model, not only enhances CVR but also realizes a scale that indicates the majority of actual buyers were accurately targeted. This precision and scale have enabled Kahoona's AI model to cover up to 3.2x the purchases compared to rule-based alternatives, translating to nearly 3.5x the ROI with the same budget - pure revenue.
The Strategic Value of AI in Remarketing
The compelling evidence provided by Kahoona's case study underscores a significant shift in remarketing strategies. The unparalleled accuracy and scale achieved through AI-driven methods offer a new perspective on budget allocation in digital marketing.
Conclusion and Call to Action
The bottom line: More revenue from the same marketing spend.
Nearly 3.5x the ROI with the same budgets.
Kahoona’s AI audiences help you fine-tune your strategies to meet your objectives: Optimized Revenue or Optimized Profit
The metrics and outcomes discussed redefine the foundations of remarketing, highlighting the profound impact of AI on enhancing both precision and scale. For businesses looking to maximize their marketing spend and drive meaningful results:
Book a meeting with one of our experts to explore the future of remarketing and how it can benefit your brand in this competitive digital landscape.
Back
Remarketing Redefined: AI-Led vs. Rule-Based Benchmark
4/17/2024
The Revolutionary Impact of AI on Conversions and Scale in Remarketing Strategies
The Paradigm Shift: From Broad to Precision Targeting
Over the last couple of years, many have leaned towards broad or “optimized” targeting via Google (P-Max) and Meta (ASC) for their remarketing efforts. This approach often stems from a lack of awareness of more effective alternatives.
However, the digital marketing landscape has evolved significantly, thanks in large part to advancements in AI technology. Even in a cookieless world, AI-driven solutions are proving to be not just viable but superior.
Case Study: AI Hyper-Segments vs. Rule-Based Segments
Kahoona's AI Predictive Segmentation model exemplifies this new era of remarketing. With 15X more data points than existing solutions, the variety of predictive segments at your disposal becomes robust.
With predictive segments such as Kahoona’s High Probability of Purchase (High PoP), intent segments, pricing level and loyalty segments, attitudinal segments (e.g., Impulsive users), psychographics (e.g., dark leather pant lovers), demographic segments (e.g., Female Gen-Z), and much more. Unlike traditional methods, Kahoona’s AI-driven approach has shown remarkable results, challenging previous benchmarks with its effectiveness.
- CVR and Scale Performance: Kahoona’s High PoP model achieved a 15% success rate in CVR, outperforming conventional strategies like Cart Abandonment-Based Remarketing (11%) and Cart Page Visit-Based Remarketing (13%). Moreover, its scale reached an impressive 89%, significantly higher than the 28% and 39% achieved by the traditional methods, respectively.
- Strategic Impact: The AI Predictive approach, specifically Kahoona’s High PoP model, not only enhances CVR but also realizes a scale that indicates the majority of actual buyers were accurately targeted. This precision and scale have enabled Kahoona's AI model to cover up to 3.2x the purchases compared to rule-based alternatives, translating to nearly 3.5x the ROI with the same budget - pure revenue.
The Strategic Value of AI in Remarketing
The compelling evidence provided by Kahoona's case study underscores a significant shift in remarketing strategies. The unparalleled accuracy and scale achieved through AI-driven methods offer a new perspective on budget allocation in digital marketing.
Conclusion and Call to Action
The bottom line: More revenue from the same marketing spend.
Nearly 3.5x the ROI with the same budgets.
Kahoona’s AI audiences help you fine-tune your strategies to meet your objectives: Optimized Revenue or Optimized Profit
The metrics and outcomes discussed redefine the foundations of remarketing, highlighting the profound impact of AI on enhancing both precision and scale. For businesses looking to maximize their marketing spend and drive meaningful results:
Book a meeting with one of our experts to explore the future of remarketing and how it can benefit your brand in this competitive digital landscape.
.png)
