Schaun Wheeler, PhD Anthropologist and Data Scientist discusses Agentic AI architectures and how they're currently being used across multiple industries to increase user engagement, retention, and conversions.
Schaun offers a unique perspective on the integration of technical solutions within business environments, shifting from a focus on traditional data science applications to an innovative approach known as the agentic customer data platform (CDP).
70-90% of machine learning projects reportedly fail to add business value or reach production stages. This failure is not due to the inadequacy of the models or their execution but primarily because businesses are often reluctant or unable to adapt their decision-making frameworks to leverage model outputs effectively.
Most companies, especially those not primarily focused on engineering, face significant challenges in integrating data science into their core operational processes. The reluctance stems from existing business models and the inherent resistance to change the decision-making basis from traditional approaches to data-driven strategies.
This drives the need for Agentic CDPs.
Unlike traditional customer data platforms, an agentic CDP doesn't just store and analyze data but actively participates in decision-making. This system uses reinforcement learning to create a dynamic model that adapts to user interactions and continuously optimizes communication strategies to enhance user engagement without increasing annoyance.
In the mobile app industry, the primary challenge has shifted from user acquisition to maintaining user attention amidst numerous competing apps. Traditional engagement strategies, heavily reliant on static rules and segments, often fail because they don't adapt to individual user behaviors and preferences. This presentation discusses how decision connectivity issues—stemming from disjointed data sources and inadequate integration—hamper effective user engagement.
The Technical Architecture of Agentic CDPs:
- Surrogates for anticipating user behavior.
- Embeddings that capture and utilize user behavior patterns.
- Edges and Weights for decision metrics.
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