Elevating Customer Engagement
Through Machine Learning and Salesforce Einstein
The AI Evolution in Retail Media
For years, personalization has been the gold standard in retail marketing. But as we head into 2025, the conversation is shifting from personalization to predictive analytics—a strategy that uses AI to forecast customer behavior and proactively shape the consumer journey. With so many NRF sessions focusing on AI’s impact—from advanced audience creation to generative creative—there’s little doubt that cutting-edge machine learning will continue to redefine retail media.
In this post, we’ll examine how AI can elevate retail media from merely “personalized” to fully “predictive,” highlighting real-world applications for Salesforce Data Cloud and Agentforce. We’ll also touch on the broader industry trends—like incrementality measurement and data clean rooms—that make AI adoption both a necessity and a competitive advantage.
Personalization 1.0 vs. Predictive Retailing
Personalization 1.0 typically focuses on tailoring product recommendations and offers based on past transactions or browsing sessions. While effective, it’s inherently reactive: the customer must first exhibit a behavior (e.g., adding an item to their cart) before the system responds.
Predictive retailing, by contrast, uses AI to anticipate a customer’s next move, sometimes before the shopper even realizes it themselves. For instance, if a loyal customer frequently buys seasonal items, a predictive system could serve them targeted promotions a month prior to the usual purchase window, capitalizing on “latent” demand. This approach not only drives higher conversion rates but also fosters a sense of discovery and delight that helps you stand out from competitors.
How Salesforce Data Cloud and Agentforce Power Predictive Retail Media
Salesforce Data Cloud unifies cross-channel data (web, mobile, in-store POS) to form a single customer profile. By pooling massive volumes of first-party data, Data Cloud creates an ideal substrate for advanced AI. This is where Agentforce steps in: it ingests these unified data sets and deploys machine learning models to surface patterns, anomalies, and untapped segments.
Example 1: Lookalike Audiences
Agentforce can identify high-value customers—based on average order value, frequency, or product categories—and then build “lookalike” profiles of consumers who exhibit similar traits. This approach refines your ad targeting so you can allocate media spend more efficiently. Paired with incrementality testing (discussed in our “Next-Generation Measurement and Attribution” post), you can gauge the true lift these lookalike campaigns deliver.
Example 2: AI-Driven Creative
As generative AI tools mature, Agentforce could dynamically produce or recommend new ad variations tailored to specific segments. If you’re running an in-store digital screen campaign in colder regions, AI might suggest promotional messaging featuring cozy winter attire, while warmer regions see a different creative. Over time, Agentforce learns from performance data to refine these dynamic creatives continuously.
Example 3: Forecasting and Inventory Planning
AI isn’t just about marketing messages—it can also inform supply chain decisions. If Agentforce detects a spike in online engagement for a soon-to-be-launched product, it could prompt your merchandising team to stock up in specific stores. By bridging marketing and operations, you ensure product availability aligns with predicted demand, reducing lost sales and improving overall customer satisfaction.
Key AI Trends at NRF 2025
Expect robust conversations around several AI-driven trends:
- Advanced Audience Creation: Beyond simple demographic segments, AI will generate micro-segments based on predictive scores for lifetime value or category affinity.
- GenAI in Creative: Retailers will experiment with text, image, and even video generation to rapidly produce ad creatives at scale.
- AI-Enhanced Incrementality: By automating the setup and execution of control vs. exposed campaigns, AI makes incrementality testing more accessible to mid-sized retailers.
- Media Planning and Budget Forecasting: Tools will emerge for real-time optimization, factoring in macro-economic indicators to adjust ad spend.
Addressing the Privacy Factor
While AI unlocks vast potential, it also underscores the importance of privacy and data security. Many retailers choose to deploy AI models via data clean rooms, ensuring that personal data isn’t exposed during audience matching and analytics. Salesforce Data Cloud supports privacy-compliant data handling, allowing you to benefit from rich insights while respecting consumer preferences and legal boundaries. For a more comprehensive look at how privacy intersects with retail media, see our “Retail Media in a Privacy-First World” post.
Getting Started: Practical Steps for Retailers
- Consolidate Your Data: Deploy Salesforce Data Cloud to build a robust single customer view.
- Identify High-Impact Use Cases: Start small with predictive campaigns that offer quick wins, like restocking recommendations or lookalike audiences.
- Integrate Agentforce: Use Agentforce’s machine learning to refine segmentation, forecast demand, and automate creative.
- Measure and Iterate: Validate results with incrementality tests, then feed those learnings back into the AI models.
- Scale: Once you see consistent performance gains, extend predictive approaches to more categories or geographies.
V2 can help with every stage—from configuring Data Cloud to customizing Agentforce —ensuring your AI initiatives are both technically sound and strategically aligned with your business goals.
Let’s Shape the Future of Retail Media Together
As personalization evolves into predictive engagement, retailers that harness AI stand to secure a significant competitive edge. By combining Salesforce Data Cloud for robust data unification with Agentforce for advanced machine learning, you can transform your retail media from reactive to proactive—delighting customers and driving sustainable growth.
Building Trust Starts With A Conversation.
If you’re ready to chart your AI roadmap or want a deep dive into how to integrate these tools seamlessly, book a meeting with us at the NRF 2025 Show (beginning January 11). V2 can guide you in building a predictive retail media framework that resonates with shoppers and delivers tangible ROI.
