Why Agentic CRM Learns What A/B Tests Can’t
Inside Eleanor Hanna’s RecSys 2025 Talk on Cross-Channel Personalization at Scale Why A/B Tests and Segmentation Don’t Cut It CRM teams today rely heavily on A/B tests and basic segmentation to optimize messaging. But this approach assumes a dangerous level of user homogeneity. As Eleanor put it, “an aggregate value can be misleading depending on […]
Why Every Event Matters
Agentic systems can only learn from what they can see. If the event stream contains only a narrow slice of user behavior, then the system’s view of the product experience is fragmented and incomplete. This paper explains why including the full range of user events – core and peripheral alike – is critical for effective […]
Behavioral Data is Key to App Personalization
Why companies need to focus on behavioral data for better personalization Customers expect more than just a well-designed app—they want a personalized experience that predicts and matches their needs, understands their context, and adapts as their unique preferences evolve. But too many companies rely on static signals like demographics, onboarding responses, or generalized user segments […]
Why Results Don’t Simply Double After Merging a 50/50 Aampe vs. Business-as-Usual Split
Many customers begin by running Aampe alongside their business-as-usual (BAU) messaging approach. A common design is a 50/50 split: after reserving a small group of users as a global holdout, half of the remaining users receive communication from Aampe, half continue with BAU. When the Aampe half shows strong results, customers sometimes have the expectation […]
Garbage In, Garbage Out? Not Anymore.
Why Agentic Systems Learn from Messy Data “Garbage in, garbage out.” It’s one of the most familiar sayings in machine learning and for traditional systems, it’s true. If your data is inconsistent, incomplete, or unstructured, your model will reflect that. That’s why so much time (and budget) is spent building data warehouses, deploying CDPs, and […]
When is AI “Agentic”? Understanding the Difference Between AI Agents, Machine Learning, and AI Decisioning
If you work with AI technology, you’ve likely encountered the following terms: Machine Learning AI Agents AI Decisioning While “machine learning” has been a part of the mainstream tech vocabulary for decades, “AI agents” and “AI decisioning” are relatively new terms. What do these words mean and how are they all related? This guide will […]
The Bigger Picture: How Aampe’s Content Maps Redefine Content Strategy
Building Relationships with Users Every human is unique—not just in their goals, but in how they move through the world to reach them. Two people can share the same destination but take completely different paths. One moves quickly, the other takes time. One seeks convenience, the other autonomy. Yet most marketing and product systems still […]
Your Brand Isn’t Fragile: How Aampe Makes Brand Integrity Scalable
What agentic systems reveal about brand control and how to build for relevance without dilution One of the most common concerns we hear from marketers, especially brand leaders, is this: “If every user sees something different, don’t we lose consistency? Doesn’t our brand get diluted?” It’s a fair question. And a powerful one. Because it […]
Better Than Targeting: Aampe’s Agentic Approach to Learning From Users
Marketers often talk about “learning what works” as if that means identifying which types of users respond best. The logic goes: find the attributes that correlate with success, target those users, and scale. That approach feels data-driven. It isn’t. A recent International Journal of Research in Marketing paper makes the weakness visible. The authors tested […]
Building a High-Performance Real-Time Query Engine with ClickHouse
System Purpose Aampe’s agentic AI infrastructure allows our customers to provision and manage an agent for every one of their end-users. If you’re a food delivery app with 10 million customers, deploying Aampe lets you provision and manage 10 million agents. While Aampe’s agent design enables them to operate a lot of causal learning, experimentation, […]