Engineering

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Schaun Wheeler

How Agentic Learners judge success without Fixed Funnels

By modeling statistical relationships between events, agents evaluate directional shifts in behavior—so the same system adapts across every lifecycle stage.

Product

Engineering

Featured
Schaun Wheeler

How Agentic Learners judge success without Fixed Funnels

By modeling statistical relationships between events, agents evaluate directional shifts in behavior—so the same system adapts across every lifecycle stage.

Product

Engineering

Featured
Schaun Wheeler

How Agentic Learners judge success without Fixed Funnels

By modeling statistical relationships between events, agents evaluate directional shifts in behavior—so the same system adapts across every lifecycle stage.

Product

Engineering

Kris Moriarty

Why AI Personalization Stalls (Even When the Models Work) — And How Aampe Unlocks Real Learning

Schaun Wheeler

Better Than Targeting: Aampe’s Agentic Approach to Learning From Users

Ben Tengelsen

When is AI "Agentic"? Understanding the Difference Between AI Agents, Machine Learning, and AI Decisioning

Team Aampe

Why Agentic CRM Learns What A/B Tests Can’t

Schaun Wheeler

How Agentic Learners handle Time-based Cycles without breaking

Schaun Wheeler

Layering A/B Testing and Agentic Learning for Better Results

Schaun Wheeler

Why Auditing a Single Agent Decision Misses the Point

Schaun Wheeler

Learning, Not Guessing: How Aampe Aligns With Every User

Schaun Wheeler

Alignment at Scale Beats Attribution at Aggregate

Schaun Wheeler

How Aampe's Agents Use Causal Analysis to Measure Impact Amidst External Messaging

Schaun Wheeler

Understanding Decision-Making in Agentic Systems

Team Aampe

Building a Multi-Tenant Event Processing Pipeline: A Dive into Pub/Sub Architecture

Schaun Wheeler

Agentic Architecture in Action: Aampe's Dual-Path Personalization Strategy

Schaun Wheeler

Evaluating Adaptive Systems: Beyond Short-Term Metrics

Schaun Wheeler

Reimagining Customer Engagement: Beyond Hierarchical Reinforcement Learning

Schaun Wheeler

Navigating User Contexts: Aampe's Approach to Personalized AI Agents

Schaun Wheeler

Beyond Multi-Armed Bandits: Understanding Aampe's Semantic-Associative Agents

Schaun Wheeler

Reevaluating Campaigns in Customer Engagement: Embracing Agentic Systems

Schaun Wheeler

Why Agentic Systems don't operate over a huge State Space

Schaun Wheeler

How Aampe Handles Model Drift with Agentic Systems

Schaun Wheeler

Unlocking the Power of Semantic-Associative Memory in AI Systems

Schaun Wheeler

Agentic AI in Enterprises

Schaun Wheeler

Having Agency vs Acting Agentically

Schaun Wheeler

JEPA and Semantic Memory: Why Current AI Models Fall Short

Schaun Wheeler

Why LLMs and RAG Aren’t Enough for Building Agentic AI Systems

Schaun Wheeler

How Agentic Systems Balance Exploration and Exploitation

Schaun Wheeler

LLMs aren't sufficient to build agentic systems

Schaun Wheeler

Five Pillars of Agentic Decision Making

Schaun Wheeler

The Explore/Exploit Tradeoff: Understanding Its True Implications in AI Systems

Schaun Wheeler

A/B Testing vs. Bandits vs. Predictive Models vs. Agentic Learners: A Stock Market Analogy

Schaun Wheeler

The Importance of Abstraction Layers in Agentic Systems

Schaun Wheeler

Why Agents Can't Learn from Historical Data: The Importance of Counterfactuals

What Is an AI Agent? Definitions, Inputs, and Outputs

Team Aampe

Adaptive Recommender Systems: A Bandit Approach

Schaun Wheeler

Optimized Messaging with Learned Weights and Beta Distribution

Team Aampe

Message Catalogs on Aampe

The Fluid Dynamics of Message Personalization

Schaun Wheeler

The LLM Alignment problem

Eleanor Hanna

You don’t know Jacc(ard)

Schaun Wheeler

Scalable Event-Based Clustering for User Segmentation

Team Aampe

How To Create A Data Pipeline Automation

Team Aampe

Best Practices for Quality ETL Testing

Team Aampe

7 Best ETL Tools in 2024

Team Aampe

The 7 Best Reverse ETL Tools in 2024

Team Aampe

Build a Robust Data Pipeline: Tips & Best Practices

Team Aampe

What is CSAT and How Do You Measure It?

Team Aampe

What Is Churn Rate & How Do You Calculate It?

Team Aampe

How to Calculate & Improve Your Customer Renewal Rate

Team Aampe

CSAT vs NPS: Understanding the Differences in Customer Satisfaction Metrics

Team Aampe

NPS vs CES: Key Differences in Customer Feedback Metrics

Team Aampe

Churn Rate vs Retention Rate

Team Aampe

Renewal Rate vs Retention Rate

Team Aampe

What is Customer Effort Score (CES) & How to Measure It?

Schaun Wheeler

Introducing Counterfactual Models: A Deeper Look at Message Timing

Team Aampe

An Unexpected Use for Gini Impurity

Team Aampe

Data Science Pipeline: Key Stages and Best Practices for Efficiency

Team Aampe

ELT vs ETL: Key Differences & Use Cases

Team Aampe

What is Reverse ETL?

Team Aampe

Data Pipeline vs ETL Pipeline: Understanding the Differences

Team Aampe

Data Pipeline Architecture: Examples & Best Practices

Team Aampe

Building a Data Warehouse: Architecture & Step-by-step Guide

Team Aampe

8 Key Steps to Effectively Build a Data Mart

Team Aampe

Top 4 Data Warehouse Tools in 2024

Team Aampe

Data Warehouse vs Data Mart: Choosing the Right Solution for Your Analytics Needs

Team Aampe

Build an Effective ETL Pipeline: Tips and Tools

Team Aampe

4 Data Pipeline Examples Across Industries

Team Aampe

How to Normalize Data in Excel

Team Aampe

How to Create a Table in BigQuery: A Step-by-Step Guide

Team Aampe

How to Connect BigQuery with Python: A Step-by-Step Guide

Team Aampe

Understanding and Measuring Net Promoter Score

Team Aampe

What is an Enterprise Data Warehouse (EDW)?

Team Aampe

Data Warehouse vs Data Lake: Key Differences Explained

Team Aampe

Data Warehouse Architecture: Foundations and Best Practices

Team Aampe

What is ETL (Extract, Transform, Load)?

Team Aampe

What is a Data Pipeline?

Team Aampe

Data Ingestion Pipeline: Building Blocks for Efficient Data Management

Team Aampe

What is Data Warehousing? Concepts, Tools, Examples

Schaun Wheeler

The hidden costs of canvas journey builders

Sami Abboud

Drawing from a random distribution in SQL

Eleanor Hanna

Using graph theory for missing data

Schaun Wheeler

“How do I know it works?” A (somewhat) technical explanation of Aampe’s learning systems

Team Aampe

How to use semantic tagging for more effective user segmentation

Schaun Wheeler

An implementation of Complement Naive Bayes for Google BigQuery

Schaun Wheeler

A walk through our conditioned experimentation process

Schaun Wheeler

How many different timing preferences could users really have?

Schaun Wheeler

How much does good timing impact ROI?

Schaun Wheeler

Conversion-based product recommendations

Schaun Wheeler

How user journeys hurt your business (with proof)

Schaun Wheeler

Mastering the art of message tone

Schaun Wheeler

How do you know which messages to write next?

Schaun Wheeler

Machine learning isn't enough

Schaun Wheeler

Using transaction data to set priors for message timing experiments

Schaun Wheeler

Structure app inventory for message-led personalization

Schaun Wheeler

User journeys and priorities

Schaun Wheeler

The challenge of tracking user activity

Schaun Wheeler

The disproportionate impact message timing has on SMS and push notification effectiveness

Schaun Wheeler

How do I know Aampe is actually working?

Schaun Wheeler

New on arXiv: validating Aampe performance with adaptive synthetic control groups

Sami Abboud

Aampe’s push notification optimized recommender system for catalog products

Schaun Wheeler

Solving model class imbalance through class construction based on coverage changepoints

Schaun Wheeler

Markov models vs. Naive Bayes: How to scale propensity models for in-app behavior and retention

Schaun Wheeler

Drop Last Touch Attribution: How Machine Learning is Changing Marketing.

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