Using Gemini For YouTube Analytics
Building a robust decision-making tool to enhance meta-analysis across the YouTube platform. We looked to design and systemize an AI utilization tool through testing, iteration, and implementation for predictive and prescriptive analytics, tailored towards specific business questions.
What Was The Challenge?
Project Goal
Build a predictive and prescriptive tool to analyze and synthesize a Meta-analysis across the YouTube platform to reveal industry insights, trends, and key market indicators through Gemini's foundational analytical model
PROJECT STAGES
Exploration
We researched, identified, and collected documentation across Google's Gemini models and compared the capabilities to peer models such as Open-AI's ChatGPT to determine viable options in testing and analytical synthesis.
Synthesis
In this stage, we compiled a plan for testing and implementation based on our knowledge of our chosen model's capabilities, designing a safe and secure way to utilize the model's analytical capabilities on our host organization's data.
Testing
The test and use of our model was structured to preserve the sensitive ownership of the host company's proprietary data. It was also used to determine the unique capabilities of the model given the structure, format, and complex nature of our data.
Implementation
Successful implementation of our initial MVP resulted in a comprehensive meta-analysis across from YouTube's source data. The insights from the test and final product revealed key industry-wide insight to answer key business-specific questions through a query approach
OUTCOME AND TAKEAWAYS
Our project revealed several key insights from initialization to design to final implementation across AI governance, data security, industry insights gathering, and safeguards for future testing.
AI Governance
Use of AI tools provided complexity in how we approached queries and revealed key insights based on inputs. Through this initial project, we established an AI governance committee to weigh relevant risks and considerations for use of the tools through testing. This process helped to provide a level of safety around the use of external AI tools on internal business processes.