What to expect from AI-driven personalization and ad accountability

Creative scaling—systematically producing marketing creatives at an increasing rate while maintaining effectiveness and relevance—is an ever-present challenge for brands. Generative artificial intelligence (Gen AI) in addition to machine learning are poised to transform that challenge by enabling enhanced personalization at scale.    

AI-powered personalization

Recent advances in AI and machine learning are making personalization at scale possible. By combining machine learning based predictive shopper insights with Gen AI capabilities, marketers can create highly customized ads, videos and other creatives for many different audiences. For example, machine learning models can analyze large quantities of historical transaction data and other relevant data to understand and predict shopper needs and intent, while Gen AI can take these learnings to create impactive and highly personalized messaging, designs, etc. that would resonate with shoppers.     Instead of showing a generic ad to all shoppers in a particular geography, brands can tailor ads for pet owners, health-conscious shoppers, eco-friendly shoppers and countless other audience groups.    In addition, AI can optimize ads for relevance in real time. If a shopper increases their interest in a certain category of products, machine learning algorithms can quickly pick up on that signal as well as channel, content and timing preference signals to refine the creative and serve it at the right time. Instead of waiting until the next major campaign, Gen AI will automatically adjust the creative to improve ad resonance. Gen AI capabilities will transform the field of dynamic creative optimization.    The potential applications of Gen AI-powered personalization include: 

• Dynamically customizing display ads by shopper interests 

• Personalizing ecommerce product recommendations  

• Tailoring email promotions based on purchase history 

• Optimizing website experiences for different personas 

AI brings new levels of accountability to media

A lack of transparency into ad performance has long plagued many ad campaigns. In addition to making personalized ads scalable at unprecedented volumes and speed, emerging AI and machine learning capabilities are revolutionizing accountability in media.    Attribution modeling and unified measurement powered by machine learning delivers a clearer measurement of the true impact of each media touchpoint within complex consumer journeys.  Automated media buying and bid adjustments enable real-time campaign optimizations based on budget utilization, conversions or other KPIs.    Together, these technologies move ad campaigns beyond vanity metrics towards actual business outcomes.  Personalized messaging informed by machine learning ensures ad relevance and thus performance. In-flight optimization prevents waste and shortens the time-to-action as the algorithms automatically uncover what works.    While adoption is still ramping up, AI-infused martech solutions demystify ad performance as they expand. For instance, Kroger Precision Marketing’s machine learning sciences drive better audience modeling and, thus, more performant campaigns. AI-optimized audiences drive strong results:*  

• 1.3x greater iROAS 

• 3.4x visits uplift 

• 3.7x sales uplift 

• 3.6x units uplift 

 *Findings based on varied client campaigns  

For brands, by infusing ad campaigns with accountability, AI brings assurances that ad budgets are driving quantifiable value. Smarter personalization, automation and predictive insights are paving the path to superior customer experiences and ROI.     Learn more about the possibilities that AI and data insights can unlock in our ebook, “5 ways data science and AI are shaping the future of grocery shopping.”