Data Engineering Techniques You Need to Maximize Your Amazon Advertising Outcomes

Have you tapped into the vast reservoir of data from Amazon's streaming APIs and various third-party applications? Curious about how smart brands harness this information to outpace their competitors? (Hint: It is not by using overpriced software)

Accessing and deciphering these readily available data sources might seem daunting to many, so let me simplify it for you. In this session, Ritu Java will offer a comprehensive step-by-step guide to seamlessly integrating data from multiple APIs and connectors so that you can customize your use of data for your own specific needs. For example for automating a gap analysis between SQP data and Ad Data, or analyzing TACoS at the SKU level, rolled up for your variation groupings.

Discover how to utilize AI to empower those without coding expertise to excel in data engineering, enhancing our advertising outcomes. Delve into a streamlined process of blending, analyzing, and leveraging your data.

Learning Objectives:

1. SOURCE DATA: What's an API? Understanding Amazon's APIs and how they can help Intermediate to Advanced sellers. What are data pipes and how can you access tools like Keepa to be informed about changes in the market
2. COMBINE DATA: Understanding how to blend data pipelines using Google Sheets and Google BigQuery. From concepts to examples, this will be the heart of the session.
3. VISUALIZE DATA: Data without visualization is like a story without pictures—full of potential, but lacking clarity and context. Learn how to use freely available Business Intelligence and data visualization tools such as Google Looker Studio to build dashboards that meet your needs